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    Wyświetlanie 1-7 z 7
    Tytuł:
    The Effect of Dual Hyperparameter Optimization on Software Vulnerability Prediction Models
    Autorzy:
    Bassi, Deepali
    Singh, Hardeep
    Powiązania:
    https://bibliotekanauki.pl/articles/2203949.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    software vulnerability
    hyperparameter optimization
    machine learning algorithm
    data balancing techniques
    data complexity measures
    Opis:
    Background: Prediction of software vulnerabilities is a major concern in the field of software security. Many researchers have worked to construct various software vulnerability prediction (SVP) models. The emerging machine learning domain aids in building effective SVP models. The employment of data balancing/resampling techniques and optimal hyperparameters can upgrade their performance. Previous research studies have shown the impact of hyperparameter optimization (HPO) on machine learning algorithms and data balancing techniques. Aim: The current study aims to analyze the impact of dual hyperparameter optimization on metrics-based SVP models. Method: This paper has proposed the methodology using the python framework Optuna that optimizes the hyperparameters for both machine learners and data balancing techniques. For the experimentation purpose, we have compared six combinations of five machine learners and five resampling techniques considering default parameters and optimized hyperparameters. Results: Additionally, the Wilcoxon signed-rank test with the Bonferroni correction method was implied, and observed that dual HPO performs better than HPO on learners and HPO on data balancers. Furthermore, the paper has assessed the impact of data complexity measures and concludes that HPO does not improve the performance of those datasets that exhibit high overlap. Conclusion: The experimental analysis unveils that dual HPO is 64% effective in enhancing the productivity of SVP models.
    Źródło:
    e-Informatica Software Engineering Journal; 2023, 17, 1; art. no. 230102
    1897-7979
    Pojawia się w:
    e-Informatica Software Engineering Journal
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of Machine Learning Techniques for Fetal Heart Rate Classification
    Autorzy:
    Cömert, Z.
    Kocamaz, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1031680.pdf
    Data publikacji:
    2017-09
    Wydawca:
    Polska Akademia Nauk. Instytut Fizyki PAN
    Tematy:
    cardiotocography
    machine learning techniques
    classification
    Opis:
    Cardiotocography is a monitoring technique providing important and vital information on fetal status during antepartum and intrapartum periods. The advances in modern obstetric practice allowed many robust and reliable machine learning techniques to be utilized in classifying fetal heart rate signals. The role of machine learning approaches in diagnosing diseases is becoming increasingly essential and intertwined. The main aim of the present study is to determine the most efficient machine learning technique to classify fetal heart rate signals. Therefore, the research has been focused on the widely used and practical machine learning techniques, such as artificial neural network, support vector machine, extreme learning machine, radial basis function network, and random forest. In a comparative way, fetal heart rate signals were classified as normal or hypoxic using the aforementioned machine learning techniques. The performance metrics derived from confusion matrix were used to measure classifiers' success. According to experimental results, although all machine learning techniques produced satisfactory results, artificial neural network yielded the rather well results with the sensitivity of 99.73% and specificity of 97.94%. The study results show that the artificial neural network was superior to other algorithms.
    Źródło:
    Acta Physica Polonica A; 2017, 132, 3; 451-454
    0587-4246
    1898-794X
    Pojawia się w:
    Acta Physica Polonica A
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Niekonwencjonalne techniki pomiarowe w modelowaniu ruchu
    Unconventional measuring techniques in traffic modeling
    Autorzy:
    Drzał, M.
    Ostaszewski, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/193434.pdf
    Data publikacji:
    2018
    Wydawca:
    Stowarzyszenie Inżynierów i Techników Komunikacji Rzeczpospolitej Polskiej
    Tematy:
    techniki pomiarowe
    modelowanie
    pozyskiwanie danych
    sieci neuronowe
    uczenie maszynowe
    automatyczne pomiary ruchu w transporcie
    measurement techniques
    modeling
    data acquisition
    neural networks
    machine learning
    automatic measurement of traffic in transport
    Opis:
    Celem artykułu jest przedstawienie autorskiego projektu wprowadzenia algorytmu opartego o sieci neuronowe w zastosowaniu do pomiarów wykonywanych w transporcie. Jakość, ilość oraz sposób pozyskiwania danych bezpośrednio przekłada się na wyniki tworzonych modeli symulacyjnych. Przeanalizowano różne systemy (zarówno komercyjne, jak i autorskie), które są używane do pozyskania danych do modelowania. W wyniku różnych wątpliwości, niedostosowania systemów lub zbyt wysokich kosztów, zaproponowano alternatywne rozwiązania, które mogą wyeliminować prezentowane problemy. Zaproponowano rozwiązania ograniczające część problemów sygnalizowanych przez autorów w przedmiotowym zakresie. Testowe prace uzasadniły wykorzystanie sieci neuronowych w pomiarach w transporcie. Otrzymano wyniki pomiarów testowych o dostatecznej zgodności z rzeczywistymi obserwacjami oraz porównano je z wynikami systemów dostępnych na rynku. Autorzy poddają analizie dalsze wymagane prace oraz możliwości udoskonalenia stosowanych rozwiązań.
    The aim of the article is to present the original project of introducing an algorithm based on neural networks in application to measurements performed in transport. The quality, quantity and method of obtaining data directly translate into the results of the simulation models created. Various systems (both commercial and proprietary) have been analyzed, which are used to obtain data for modeling. As a result of various doubts, system maladjustments or excessive costs, alternative solutions have been proposed that can eliminate the presented problems. As part of its work, solutions have been proposed that limit some of the problems reported by the authors in this regard. Test work justified the use of neural networks in measurements in transport. Test results with sufficient compliance with real observations were obtained and compared to the results of systems available on the market. The authors also analyze further required work and the possibilities of improving the solutions used.
    Źródło:
    Transport Miejski i Regionalny; 2018, 4; 25-31
    1732-5153
    Pojawia się w:
    Transport Miejski i Regionalny
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Systematic analysis and review of video object retrieval techniques
    Autorzy:
    Ghuge, C. A.
    Prakash, V. Chandra
    Ruikar, Sachin D.
    Powiązania:
    https://bibliotekanauki.pl/articles/2050246.pdf
    Data publikacji:
    2020
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    video object retrieval
    computer vision
    deep learning
    fuzzy-based techniques
    machine learning
    query-based techniques
    graph-based techniques
    Opis:
    Video object retrieval is a promising research direction, developing in the recent years, and the current video object retrieval strategies are used for visualizing, digitizing, modeling, and retrieving the objects especially in graphics and in architectural design. The research performed led to the design of proficient video object retrieval techniques. Yet, although, a number of algorithms had been devised for tracking objects, the problems persist in enhancing the performance, for instance – with regard to non-rigid objects. In this review article we provide a detailed survey of 50 research papers presenting the suggested video object retrieval methodologies, based on approaches such as deep learning techniques, graph-based techniques, query-based techniques, feature-based techniques, fuzzybased techniques, machine learning-based techniques, distance metric learning-based technique, and also other ones. Moreover, analysis and discussion are presented concerning the year of publication, employed methodology, evaluation metrics, accuracy range, adopted framework, datasets utilized, and the implementation tool. Finally, the research gaps and issues related to various proposed video object retrieval schemes are presented for guiding the researchers towards improved contributions to the video object retrieval methods.
    Źródło:
    Control and Cybernetics; 2020, 49, 4; 471-498
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forecasting techniques in construction industry: earned value indicators and performance models
    Autorzy:
    Jaber, Firas K.
    Jasim, Nidal A.
    Al-Zwainy, Faiq M.
    Powiązania:
    https://bibliotekanauki.pl/articles/118685.pdf
    Data publikacji:
    2020
    Wydawca:
    Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
    Tematy:
    Machine Learning Regression Techniques
    MLRT
    earned value indexes
    SPI
    CPI
    TCPI
    Opis:
    Machine Learning Regression Techniques (MLRT) as a shrewd method can be utilized in this study being exceptionally fruitful in demonstrating non-linear and the interrelationships among them in problems of construction projects such as the earned value indexes for tall buildings projects in Republic of Iraq. Three forecasting models were developed to foresee Schedule Performance Index (SPI) as first model, Cost Performance Index (CPI) as a second model, and the third model is To Complete Cost Performance Indicator (TCPI) in Bismayah New City was chosen as a case study. The methodology is mainly impacted by the deciding various components (variables) which impact on the earned value analysis, six free factors (X1: BAC, Budget at Completion; X2: AC, Actual Cost; X3: A%, Actual Percentage; X4: EV, Earned Value; X5: P%, Planning Percentage, and X6: PV, Planning Value) were self-assertively assigned and agreeably depicted for per tall buildings projects. It was found that the MLRT showed good results of estimation in terms of correlation coefficient (R) generated by MLR models for SPI and CPI and TCPI where the R were 85.5%, 89.2%, and 86.3% respectively. At long last, a result tends to be presumed that these models show a brilliant concurrence with the genuine estimations.
    Źródło:
    Scientific Review Engineering and Environmental Sciences; 2020, 29, 2; 234-243
    1732-9353
    Pojawia się w:
    Scientific Review Engineering and Environmental Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Reinforcement Learning in Ship Handling
    Autorzy:
    Łącki, M.
    Powiązania:
    https://bibliotekanauki.pl/articles/117361.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
    Tematy:
    Ship Handling
    Reinforcement Learning
    Machine Learning Techniques
    Manoeuvring
    Restricted Waters
    Markov Decision Process (MDP)
    Artificial Neural Network (ANN)
    multi-agent environment
    Opis:
    This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.
    Źródło:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2008, 2, 2; 157-160
    2083-6473
    2083-6481
    Pojawia się w:
    TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparative Analysis of Classifiers for the Assessment of Respiratory Disorders Using Speech Parameters
    Autorzy:
    Shrivastava, Poonam
    Tripathi, Neeta
    Singh, Bikesh Kumar
    Dewangan, Bhupesh Kumar
    Powiązania:
    https://bibliotekanauki.pl/articles/31339918.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    healthy speech
    affected speech
    machine learning
    classification techniques
    respiratory disorders
    speech analysis
    Opis:
    Non-invasive techniques for the assessment of respiratory disorders have gained increased importance in recent years due to the complexity of conventional methods. In the assessment of respiratory disorders, machine learning may play a very essential role. Respiratory disorders lead to variation in the production of speech as both go hand in hand. Thus, speech analysis can be a useful means for the pre-diagnosis of respiratory disorders. This article aims to develop a machine learning approach to differentiate healthy speech from speech corresponding to different respiratory disorders (affected). Thus, in the present work, a set of 15 relevant and efficient features were extracted from acquired data, and classification was done using different classifiers for healthy and affected speech. To assess the performance of different classifiers, accuracy, specificity (Sp), sensitivity (Se), and area under the receiver operating characteristic curve (AUC) was used by applying both multi-fold cross-validation methods (5-fold and 10-fold) and the holdout method. Out of the studied classifiers, decision tree, support vector machine (SVM), and k-nearest neighbor (KNN) were found more appropriate in providing correct assessment clinically while considering 15 features as well as three significant features (Se > 89%, Sp > 89%, AUC> 82%, and accuracy > 99%). The conclusion was that the proposed classifiers may provide an aid in the simple assessment of respiratory disorders utilising speech parameters with high efficiency. In the future, the proposed approach can be evaluated for the detection of specific respiratory disorders such as asthma, COPD, etc.
    Źródło:
    Archives of Acoustics; 2023, 48, 1; 13-24
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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