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    Wyświetlanie 1-10 z 10
    Tytuł:
    Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers
    Autorzy:
    Biju, V. G.
    Prashanth, CM
    Powiązania:
    https://bibliotekanauki.pl/articles/108646.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    bagging
    boosting
    SVM
    KNN
    decision tree
    Opis:
    This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.
    Źródło:
    Journal of Applied Computer Science Methods; 2017, 9 No. 1; 23-47
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Intelligent touch based user authentication
    Autorzy:
    Mazur, D.
    Tybura, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/114728.pdf
    Data publikacji:
    2017
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    touch
    mobile
    authentication
    authorization
    SVM
    kNN
    kmeans
    neural networks
    Opis:
    Many researches had shown that touch based authentication is something possible to implement in many devices. This research focuses mainly on making a progress in this field by using more advanced methods such as SVM, kNN, kmeans or neural networks in attempt to build system for both recognizing and learning user’s behavior.
    Źródło:
    Measurement Automation Monitoring; 2017, 63, 1; 20-23
    2450-2855
    Pojawia się w:
    Measurement Automation Monitoring
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Resource optimisation in cloud computing: comparative study of algorithms applied to recommendations in a big data analysis architecture
    Autorzy:
    Ndayikengurukiye, Aristide
    Ez-Zahout, Abderrahmane
    Aboubakr, Akou
    Charkaoui, Youssef
    Fouzia, Omary
    Powiązania:
    https://bibliotekanauki.pl/articles/2141815.pdf
    Data publikacji:
    2021
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    cloud computing
    Big Data
    IoT
    recommender system
    KNN algorithm
    Opis:
    Recommender systems (RS) have emerged as a means of providing relevant content to users, whether in social networking, health, education, or elections. Furthermore, with the rapid development of cloud computing, Big Data, and the Internet of Things (IoT), the component of all this is that elections are controlled by open and accountable, neutral, and autonomous election management bodies. The use of technology in voting procedures can make them faster, more efficient, and less susceptible to security breaches. Technology can ensure the security of every vote, better and faster automatic counting and tallying, and much greater accuracy. The election data were combined by different websites and applications. In addition, it was interpreted using many recommendation algorithms such as Machine Learning Algorithms, Vector Representation Algorithms, Latent Factor Model Algorithms, and Neighbourhood Methods and shared with the election management bodies to provide appropriate recommendations. In this paper, we conduct a comparative study of the algorithms applied in the recommendations of Big Data architectures. The results show us that the K-NN model works best with an accuracy of 96%. In addition, we provided the best recommendation system is the hybrid recommendation combined by content-based filtering and collaborative filtering uses similarities between users and items.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 65-75
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Zastosowanie multimodalnej klasyfikacji w rozpoznawaniu stanów emocjonalnych na podstawie mowy spontanicznej
    Spontaneus emotion redognition from speech signal using multimodal classification
    Autorzy:
    Kamińska, D.
    Pelikant, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/408014.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej
    Tematy:
    rozpoznawanie emocji
    sygnał mowy
    algorytm kNN
    emotion recognition
    speech signal
    k-NN algorithm
    Opis:
    Artykuł prezentuje zagadnienie związane z rozpoznawaniem stanów emocjonalnych na podstawie analizy sygnału mowy. Na potrzeby badań stworzona została polska baza mowy spontanicznej, zawierająca wypowiedzi kilkudziesięciu osób, w różnym wieku i różnej płci. Na podstawie analizy sygnału mowy stworzono przestrzeń cech. Klasyfikację stanowi multimodalny mechanizm rozpoznawania, oparty na algorytmie kNN. Średnia poprawność: rozpoznawania wynosi 83%.
    The article presents the issue of emotion recognition from a speech signal. For this study, a Polish spontaneous database, containing speech from people of different age and gender, was created. Features were determined from the speech signal. The process of recognition was based on multimodal classification, related to kNN algorithm. The average of accuracy performance was up to 83%.
    Źródło:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2012, 3; 36-39
    2083-0157
    2391-6761
    Pojawia się w:
    Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Hough transform in the classification process of inland ships
    Autorzy:
    Bobkowska, Katarzyna
    Wawrzyniak, Natalia
    Powiązania:
    https://bibliotekanauki.pl/articles/135214.pdf
    Data publikacji:
    2019
    Wydawca:
    Akademia Morska w Szczecinie. Wydawnictwo AMSz
    Tematy:
    Hough transform
    k Nearest Neighbors (kNN)
    image processing
    classification
    ship recognition
    line detection
    Opis:
    This article presents an analysis of the possibilities of using image processing methods for feature extraction that allows kNN classification based on a ship’s image delivered from an on-water video surveillance system. The subject of the analysis is the Hough transform which enables the detection of straight lines in an image. The recognized straight lines and the information about them serve as features in the classification process. Above all, this approach allows ships to be recognized, which can then be characterized by a specific representation and shape. Recreational units that are often seen on inland waters were classified correctly using this method. Each analyzed camera image was previously prepared – brought to the form where the ship was visible from the side and the background removed (they were monochromatic – white). The results obtained in this work will allow for the development of the final ship classification method based on camera images. This method is a significant part of the emerging system prototype, which is implemented as part of the Automatic Ship Recognition and Identification (SHREC) project.
    Źródło:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2019, 58 (130); 9-15
    1733-8670
    2392-0378
    Pojawia się w:
    Zeszyty Naukowe Akademii Morskiej w Szczecinie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Instance based kNN modification for classification of medical data
    Autorzy:
    Orczyk, T.
    Porwik, P.
    Lewandowski, M.
    Cholewa, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/333353.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
    Tematy:
    k Nearest Neighbors
    kNN
    unbalanced data
    incomplete data
    algorytm najbliższych sąsiadów
    dane niesymetryczne
    dane niekompletne
    Opis:
    Paper describes a novel modification to a well known kNN algorithm, which enables using it for medical data, which often is a class-imbalanced data with randomly missing values. Paper presents the modified algorithm details, experiment setup, results obtained on a cross validated classification of a benchmark database with randomly removed values (missing data) and records (class imbalance), and their comparison with results of the state of the art classification algorithms.
    Źródło:
    Journal of Medical Informatics & Technologies; 2015, 24; 99-106
    1642-6037
    Pojawia się w:
    Journal of Medical Informatics & Technologies
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Similarity-based methods : a general framework for classification, approximation and association
    Autorzy:
    Duch, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/206007.pdf
    Data publikacji:
    2000
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    aproksymacja
    klasyfikacja
    optymalizacja
    pamięć asocjacyjna
    approximation
    associative memory
    classification
    feature selection
    kNN
    optimization
    similarity-based methods
    Opis:
    Similarity-based methods (SBM) are a generalization of the minimal distance (MD) methods which form a basis of several machine learning and pattern recognition methods. Investigation of similarity leads to a fruitful framework in which many classification, approximation and association methods are accommodated. Probability p(C|X; M) of assigning class C to a vector X, given a classification model M, depends on adaptive parameters and procedures used in construction of the model. Systematic overview of choices available for model building is presented and numerous improvements suggested. Similarity-Based Methods have natural neural-network type realizations. Such neural network models as the Radial Basis Functions (RBF) and the Multilayer Perceptrons (MLPs) are included in this framework as special cases. SBM may also include several different submodels and a procedure to combine their results. Many new versions of similarity-based methods are derived from this framework. A search in the space of all methods belonging to the SBM framework finds a particular combination of parameterizations and procedures that is most appropriate for a given data. No single classification method can beat this approach. Preliminary implementation of SBM elements tested on a real-world datasets gave very good results.
    Źródło:
    Control and Cybernetics; 2000, 29, 4; 937-967
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of selected classification methods in automated oak seed sorting
    Porównanie wybranych metod klasyfikacji w automatycznym sortowaniu nasion dębu
    Autorzy:
    Grabska-Chrząstowska, J.
    Kwiecień, J.
    Drożdż, M.
    Bubliński, Z.
    Tadeusiewicz, R.
    Szczepaniak, J.
    Walczyk, J.
    Tylek, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/336489.pdf
    Data publikacji:
    2017
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Maszyn Rolniczych
    Tematy:
    acorn classification
    automatic sorting
    acorn
    image analysis
    image processing
    kNN
    ANN
    SVM
    klasyfikacja żołędzi
    automatyczne sortowanie
    żołędzie
    przetwarzanie obrazu
    analiza obrazu
    Opis:
    In this paper the results of automated, vision based classification of oak seeds viability i.e. their ability to germinate are presented. In the first stage, using a photo of the seed cross-section, a set of feature vectors were determined. Then three classification methods were examined: k-nearest neighbours (k-NNs), artificial neural networks (ANNs) and support vector machines (SVMs). Finally, a 73.1% precision was obtained for kNN and a 64 bin histogram, 78.5% for ANN and a 4 bin histogram and 78.8% for SVM with a 64 bin histogram.
    W artykule zaprezentowano wyniki badań automatycznej, wizyjnej klasyfikacji nasion dębu pod względem ich żywotności, tj. zdolności do kiełkowania. W pierwszym etapie prac, na podstawie zdjęcia przekroju nasiona, wyznaczono zbiór cech, który w sposób niezależny od kształtu i rozmiaru poszczególnych obiektów pozwala na opisanie ich budowy anatomicznej. Następnie zbadano, dla wyselekcjonowanych wektorów cech, trzy metody klasyfikacji: k-najbliższych sąsiadów (k-NN), artificial neural networks (ANN) oraz maszynę wektorów nośnych (SVM). Uzyskano 73,1% precyzji rozpoznawania dla histogramu o długości 64 metodą kNN, 78,5% dla histogramu o długości 4 dla ANN i 78,8% dla histogramu o długości 64 metodą SVM.
    Źródło:
    Journal of Research and Applications in Agricultural Engineering; 2017, 62, 1; 31-33
    1642-686X
    2719-423X
    Pojawia się w:
    Journal of Research and Applications in Agricultural Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Deep-Learning-Based Bug Priority Prediction Using RNN-LSTM Neural Networks
    Autorzy:
    Bani-Salameh, Hani
    Sallam, Mohammed
    Al shboul, Bashar
    Powiązania:
    https://bibliotekanauki.pl/articles/1818480.pdf
    Data publikacji:
    2021
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    assigning
    priority
    bug tracking systems
    bug priority
    bug severity
    closed-source
    data mining
    machine learning
    ML
    deep learning
    RNN-LSTM
    SVM
    KNN
    Opis:
    Context: Predicting the priority of bug reports is an important activity in software maintenance. Bug priority refers to the order in which a bug or defect should be resolved. A huge number of bug reports are submitted every day. Manual filtering of bug reports and assigning priority to each report is a heavy process, which requires time, resources, and expertise. In many cases mistakes happen when priority is assigned manually, which prevents the developers from finishing their tasks, fixing bugs, and improve the quality. Objective: Bugs are widespread and there is a noticeable increase in the number of bug reports that are submitted by the users and teams’ members with the presence of limited resources, which raises the fact that there is a need for a model that focuses on detecting the priority of bug reports, and allows developers to find the highest priority bug reports. This paper presents a model that focuses on predicting and assigning a priority level (high or low) for each bug report. Method: This model considers a set of factors (indicators) such as component name, summary, assignee, and reporter that possibly affect the priority level of a bug report. The factors are extracted as features from a dataset built using bug reports that are taken from closed-source projects stored in the JIRA bug tracking system, which are used then to train and test the framework. Also, this work presents a tool that helps developers to assign a priority level for the bug report automatically and based on the LSTM’s model prediction. Results: Our experiments consisted of applying a 5-layer deep learning RNN-LSTM neural network and comparing the results with Support Vector Machine (SVM) and K-nearest neighbors (KNN) to predict the priority of bug reports. The performance of the proposed RNN-LSTM model has been analyzed over the JIRA dataset with more than 2000 bug reports. The proposed model has been found 90% accurate in comparison with KNN (74%) and SVM (87%). On average, RNN-LSTM improves the F-measure by 3% compared to SVM and 15.2% compared to KNN. Conclusion: It concluded that LSTM predicts and assigns the priority of the bug more accurately and effectively than the other ML algorithms (KNN and SVM). LSTM significantly improves the average F-measure in comparison to the other classifiers. The study showed that LSTM reported the best performance results based on all performance measures (Accuracy = 0.908, AUC = 0.95, F-measure = 0.892).
    Źródło:
    e-Informatica Software Engineering Journal; 2021, 15, 1; 29--45
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Detection of distributed denial of service attacks for IoT-based healthcare systems
    Autorzy:
    Kaur, Gaganjot
    Gupta, Prinima
    Powiązania:
    https://bibliotekanauki.pl/articles/38701793.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Podstawowych Problemów Techniki PAN
    Tematy:
    software defined networking
    k-nearest neighbors
    distributed denial of service
    DPTCM-KNN approach
    SVM
    sieci definiowane programowo
    k-najbliższy sąsiad
    rozproszona odmowa usługi
    Opis:
    One of the major common assaults in the current Internet of things (IoT) network-based healthcare infrastructures is distributed denial of service (DDoS). The most challenging task in the current environment is to manage the creation of vast multimedia data from the IoT devices, which is difficult to be handled solely through the cloud. As the software defined networking (SDN) is still in its early stages, sampling-oriented measurement techniques used today in the IoT network produce low accuracy, increased memory usage, low attack detection, higher processing and network overheads. The aim of this research is to improve attack detection accuracy by using the DPTCM-KNN approach. The DPTCMKNN technique outperforms support vector machine (SVM), yet it still has to be improved. For healthcare systems, this work develops a unique approach for detecting DDoS assaults on SDN using DPTCM-KNN.
    Źródło:
    Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 167-186
    2299-3649
    Pojawia się w:
    Computer Assisted Methods in Engineering and Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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