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    Tytuł:
    A machine learning approach for the segmentation of driving maneuvers and its application in autonomous parking
    Autorzy:
    Notomista, G.
    Botsch, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91543.pdf
    Data publikacji:
    2017
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    autonomous parking
    ensemble learning
    maneuver segmentation
    Opis:
    A classification system for the segmentation of driving maneuvers and its validation in autonomous parking using a small-scale vehicle are presented in this work. The classifiers are designed to detect points that are crucial for the path-planning task, thus enabling the implementation of efficient autonomous parking maneuvers. The training data set is generated by simulations using appropriate vehicle–dynamics models and the resulting classifiers are validated with the small-scale autonomous vehicle. To achieve both a high classification performance and a classification system that can be implemented on a microcontroller with limited computational resources, a two-stage design process is applied. In a first step an ensemble classifier, the Random Forest (RF) algorithm, is constructed and based on the RF-kernel a General Radial Basis Function (GRBF) classifier is generated. The GRBF-classifier is integrated into the small-scale autonomous vehicle leading to excellent performance in parallel-, cross- and oblique-parking maneuvers. The work shows that segmentation using classifies and open-loop control are an efficient approach in autonomous driving for the implementation of driving maneuvers.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 4; 243-255
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48
    Autorzy:
    Otoom, Mohammad Mahmood
    Sattar, Khalid Nazim Abdul
    Al Sadig, Mutasim
    Powiązania:
    https://bibliotekanauki.pl/articles/2201908.pdf
    Data publikacji:
    2023
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Mechaników Polskich
    Tematy:
    cyber security
    network intrusion
    ensemble learning
    machine learning
    ML
    Opis:
    Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.
    Źródło:
    Advances in Science and Technology. Research Journal; 2023, 17, 2; 322--329
    2299-8624
    Pojawia się w:
    Advances in Science and Technology. Research Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Podejście wielomodelowe w regresji danych symbolicznych interwałowych
    Ensemble learning in regression model of symbolic interval data
    Autorzy:
    Pełka, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/424829.pdf
    Data publikacji:
    2014
    Wydawca:
    Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
    Tematy:
    ensemble learning
    regression of symbolic data
    interval-valued data
    Opis:
    Ensemble learning, which consist in using a lot of models (instead one single model) can be used in classical data analysis. The aim of the paper is to present an adaptation of ensemble learning with the use of bagging for regression analysis of symbolic interval-valued data. The article presents basic concepts concerning symbolic data analysis, the adaptation of ordinary least squares model for symbolic interval-valued data and the idea of bagging approach in ensemble learning. The empirical part contains the results of simulation studies obtained with the application of real and artificial data sets for centers and centers and range methods. The results show that both methods reach usually better results when using bagging than in case of a single model.
    Źródło:
    Econometrics. Ekonometria. Advances in Applied Data Analytics; 2014, 4(46); 211-220
    1507-3866
    Pojawia się w:
    Econometrics. Ekonometria. Advances in Applied Data Analytics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Research on the risk classification of cruise ship fires based on an attention-BP neural network
    Autorzy:
    Xiong, Zhenghua
    Xiang, Bo
    Chen, Ye
    Chen, Bin
    Powiązania:
    https://bibliotekanauki.pl/articles/32912853.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    cruise fire
    simulation modeling
    ensemble learning
    BP neural network
    Opis:
    Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.
    Źródło:
    Polish Maritime Research; 2022, 3; 61-68
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applying a neural network ensemble to intrusion detection
    Autorzy:
    Ludwig, Simone A.
    Powiązania:
    https://bibliotekanauki.pl/articles/91620.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    ensemble learning
    Deep Neural Networks
    NSL-KDD data set
    Opis:
    An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 3; 177-178
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Projekt badawczy, uczenie zespołowe, teoria ugruntowana
    Realization of research project in the light of team learning practice
    Autorzy:
    Janiszewski, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/323831.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
    Tematy:
    projekt badawczy
    uczenie zespołowe
    teoria ugruntowana
    research project
    ensemble learning
    grounded theory
    Opis:
    W artykule podjęto tematykę uczenia się w zespole. Omówiono zagadnienia teoretyczne, propozycję budowy narzędzia badawczego oraz wyniki badania wstępnego. Usiłowano uzyskać odpowiedź na pytanie badawcze dotyczące sposobów i środków wykorzystywanych przez członków zespołu badawczego dążących do uzyskania namacalnych wyników pracy w warunkach intensywnych i wielokierunkowych przepływów wiedzy. Uwaga została skoncentrowana na specyfice pracy zespołów realizujących projekt badawczy dofinansowywany przez Narodowe Centrum Nauki. Uzyskane wyniki mogą posłużyć w przyszłości jako podstawa do przeprowadzenia badań związanych z funkcjonowaniem zespołów badawczych NCN w całym kraju, co z uwagi na obecne trendy w zakresie realizacji badań można ocenić jako przedsięwzięcie warte podjęcia.
    In the paper author discusses the topic connected with team learning issues. Theoretical problems are analysed as well as the proposition of the way of building of a research tool is presented. Next, results of initial research are shown. The authors tries to find the answer for the research question related to the ways and means which are made use of by team members who deal with intensive and multidirectional knowledge flows in order to obtain some tangible results of their work. The research object which attention is paid to is research teams that carry out research projects financed by National Science Centre. Hitherto obtained results may allow author to conduct research on NCN’s research teams in all country in the future. Taking into account current trends as to ways of conducting research this kind of undertaking appears to be worth implementing.
    Źródło:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska; 2018, 117; 187-198
    1641-3466
    Pojawia się w:
    Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A Systematic Review of Ensemble Techniques for Software Defect and Change Prediction
    Autorzy:
    Khanna, Megha
    Powiązania:
    https://bibliotekanauki.pl/articles/2123249.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    ensemble learning
    software change prediction
    software defect prediction
    software quality
    systematic review
    Opis:
    Background: The use of ensemble techniques have steadily gained popularity in several software quality assurance activities. These aggregated classifiers have proven to be superior than their constituent base models. Though ensemble techniques have been widely used in key areas such as Software Defect Prediction (SDP) and Software Change Prediction (SCP), the current state-of-the-art concerning the use of these techniques needs scrutinization. Aim: The study aims to assess, evaluate and uncover possible research gaps with respect to the use of ensemble techniques in SDP and SCP. Method: This study conducts an extensive literature review of 77 primary studies on the basis of the category, application, rules of formulation, performance, and possible threats of the proposed/utilized ensemble techniques. Results: Ensemble techniques were primarily categorized on the basis of similarity, aggregation, relationship, diversity, and dependency of their base models. They were also found effective in several applications such as their use as a learning algorithm for developing SDP/SCP models and for addressing the class imbalance issue. Conclusion: The results of the review ascertain the need of more studies to propose, assess, validate, and compare various categories of ensemble techniques for diverse applications in SDP/SCP such as transfer learning and online learning.
    Źródło:
    e-Informatica Software Engineering Journal; 2022, 16, 1; art. no. 220105
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Ensemble learning techniques for transmission quality classification in a Pay&Require multi-layer network
    Autorzy:
    Żelasko, Dariusz
    Pławiak, Paweł
    Powiązania:
    https://bibliotekanauki.pl/articles/1838182.pdf
    Data publikacji:
    2021
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    Pay&Require
    ensemble learning
    machine learning
    resource allocation
    QoS
    uczenie zespołowe
    uczenie maszynowe
    alokacja zasobu
    Opis:
    Due to a continuous increase in the use of computer networks, it has become important to ensure the quality of data transmission over the network. The key issue in the quality assurance is the translation of parameters describing transmission quality to a certain rating scale. This article presents a technique that allows assessing transmission quality parameters. Thanks to the application of machine learning, it is easy to translate transmission quality parameters, i.e., delay, bandwidth, packet loss ratio and jitter, into a scale understandable by the end user. In this paper we propose six new ensembles of classifiers. Each classification algorithm is combined with preprocessing, cross-validation and genetic optimization. Most ensembles utilize several classification layers in which popular classifiers are used. For the purpose of the machine learning process, we have created a data set consisting of 100 samples described by four features, and the label which describes quality. Our previous research was conducted with respect to single classifiers. The results obtained now, in comparison with the previous ones, are satisfactory—high classification accuracy is reached, along with 94% sensitivity (overall accuracy) with 6/100 incorrect classifications. The suggested solution appears to be reliable and can be successfully applied in practice.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 135-153
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A comparative study on performance of basic and ensemble classifiers with various datasets
    Autorzy:
    Gunakala, Archana
    Shahid, Afzal Hussain
    Powiązania:
    https://bibliotekanauki.pl/articles/30148255.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    classification
    Naïve Bayes
    neural network
    Support Vector Machine
    Decision Tree
    ensemble learning
    Random Forest
    Opis:
    Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.
    Źródło:
    Applied Computer Science; 2023, 19, 1; 107-132
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Boosting-based model for solving Sm-Co alloy’s maximum energy product prediction task
    Autorzy:
    Trostianchyn, A.M.
    Izonin, I.V.
    Duriagina, Z.A.
    Tkachenko, R.O.
    Kulyk, V.V.
    Havrysh, B.M.
    Powiązania:
    https://bibliotekanauki.pl/articles/24200577.pdf
    Data publikacji:
    2022
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    Sm-Co alloy
    ensemble learning
    gradient boosting
    prediction accuracy
    Stop Sm-Co
    uczenie zespołowe
    dokładność przewidywania
    Opis:
    Purpose: This paper aims to decide the Sm-Co alloy’s maximum energy product prediction task based on the boosting strategy of the ensemble of machine learning methods. Design/methodology/approach: This paper examines an ensemble-based approach to solving Sm-Co alloy’s maximum energy product prediction task. Because classical machine learning methods sometimes do not supply acceptable precision when solving the regression problem, the authors investigated the boosting ML model, namely Gradient Boosting. Building a boosting model based on several weak submodels, each of which considers the errors of the prior ones, provides substantial growth in the accuracy of the problem-solving. The obtained result is confirmed using an actual data set collected by the authors. Findings: This work demonstrates the high efficiency of applying the ensemble strategy of machine learning to the applied problem of materials science. The experiments determined the highest accuracy of solving the forecast task for the maximum energy product of Sm-Co alloy formed on the boosting model of machine learning in comparison with classical methods of machine learning. Research limitations/implications: The boosting strategy of machine learning, in comparison with single algorithms of machine learning, requires much more computational and time resources to implement the learning process of the model. Practical implications: This work demonstrated the possibility of effectively solving Sm-Co alloy’s maximum energy product prediction task using machine learning. The studied boosting model of machine learning for solving the problem provides high accuracy of prediction, which reveals several advantages of their use in solving issues applied to computational material science. Furthermore, using the Orange modelling environment provides a simple and intuitive interface for using the researched methods. The proposed approach to the forecast significantly reduces the time and resource costs associated with studying expensive rare earth metals (REM)-based ferromagnetic materials. value: The authors have collected and formed a set of data on predicting the maximum energy product of the Sm-Co alloy. We used machine learning tools to solve the task. As a result, the most increased forecasting precision based on the boosting model is demonstrated compared to classical machine learning methods.
    Źródło:
    Archives of Materials Science and Engineering; 2022, 116, 2; 71--80
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-10 z 10

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