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    Tytuł:
    Hybrid feature selection and support vector machine framework for predicting maintenance failures
    Autorzy:
    Tarik, Mouna
    Mniai, Ayoub
    Jebari, Khalid
    Powiązania:
    https://bibliotekanauki.pl/articles/30148252.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Promocji Wiedzy
    Tematy:
    predictive maintenance
    machine learning
    features selection
    SMOTE-Tomek
    Support Vector Machine
    Opis:
    The main aim of predictive maintenance is to minimize downtime, failure risks and maintenance costs in manufacturing systems. Over the past few years, machine learning methods gained ground with diverse and successful applications in the area of predictive maintenance. This study shows that performing preprocessing techniques such as over¬sampling and feature selection for failure prediction is promising. For instance, to handle imbalanced data, the SMOTE-Tomek method is used. For feature selection, three different methods can be applied: Recursive Feature Elimination, Random Forest and Variance Threshold. The data considered in this paper for simulation are used in literature. They are used to measure aircraft engine sensors to predict engine failures, while the prediction algorithm used is a Support Vector Machine. The results show that classification accuracy can be significantly boosted by using the preprocessing techniques.
    Źródło:
    Applied Computer Science; 2023, 19, 2; 112-124
    1895-3735
    2353-6977
    Pojawia się w:
    Applied Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Two-stage classification approach for human detection in camera video in bulk ports
    Autorzy:
    Mi, C.
    Zhang, Z.
    He, X.
    Huang, Y.
    Mi, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/259499.pdf
    Data publikacji:
    2015
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    Human Detection
    Histograms of Oriented Gradients
    Support Vector Machine
    classification
    Opis:
    With the development of automation in ports, the video surveillance systems with automated human detection begun to be applied in open-air handling operation areas for safety and security. The accuracy of traditional human detection based on the video camera is not high enough to meet the requirements of operation surveillance. One of the key reasons is that Histograms of Oriented Gradients (HOG) features of the human body will show great different between front & back standing (F&B) and side standing (Side) human body. Therefore, the final training for classifier will only gain a few useful specific features which have contribution to classification and are insufficient to support effective classification, while using the HOG features directly extracted by the samples from different human postures. This paper proposes a two-stage classification method to improve the accuracy of human detection. In the first stage, during preprocessing classification, images is mainly divided into possible F&B human body and not F&B human body, and then they were put into the second-stage classification among side human and non-human recognition. The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.
    Źródło:
    Polish Maritime Research; 2015, S 1; 163-170
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms
    Autorzy:
    Nandika, Muhammad Rizki
    Ulfa, Azura
    Ibrahim, Andi
    Purwanto, Anang Dwi
    Powiązania:
    https://bibliotekanauki.pl/articles/8413878.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    accuracy
    coral
    seagrass
    Maximum Likelihood
    Minimum Distance
    Support Vector Machine
    remote sensing
    Opis:
    Remote sensing technology is reliable in identifying the distribution of seabed cover yet there are still challenges in retrieving the data collection of shallow water habitats than with other objects on land. Classification algorithms based on remote sensing technology have been developed for application to map benthic habitats, such as Maximum Likelihood, Minimum Distance, and Support Vector Machine. This study focuses on examining those three classification algorithms to retrieve information on the benthic habitat in Pari Island, Jakarta using visual interpretation data for classification, and data field measurements for accuracy testing. This study used five classes of benthic objects, namely sand, sand-seagrass, rubble, seagrass, and coral. The results show how the proposed approach in this study provides an overall good classification of marine habitat with an accuracy produced 63.89–81.95%. The Support Vector Machine algorithm produced the highest accuracy rate of about 81.95%. The Support Vector Machine algorithm at a very high spatial resolution is considered to be capable of identifying, monitoring, and performing the rapid assessment of benthic habitat objects.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 69--87
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    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:
    Impact of the COVID-19 pandemic on the expression of emotions in social media
    Autorzy:
    Ghosh, Debabrata
    Powiązania:
    https://bibliotekanauki.pl/articles/2027766.pdf
    Data publikacji:
    2020
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    Classification
    COVID-19
    Emotion
    Emotion analysis
    Naïve Bayes
    Pandemic
    Random Forest
    Support Vector Machine
    Opis:
    In the age of social media, every second thousands of messages are exchanged. Analyzing those unstructured data to find out specific emotions is a challenging task. Analysis of emotions involves evaluation and classification of text into emotion classes such as Happy, Sad, Anger, Disgust, Fear, Surprise, as defined by emotion dimensional models which are described in the theory of psychology (www 1; Russell, 2005). The main goal of this paper is to cover the COVID-19 pandemic situation in India and its impact on human emotions. As people very often express their state of the mind through social media, analyzing and tracking their emotions can be very effective for government and local authorities to take required measures. We have analyzed different machine learning classification models, such as Naïve Bayes, Support Vector Machine, Random Forest Classifier, Decision Tree and Logistic Regression with 10-fold cross validation to find out top ML models for emotion classification. After tuning the Hyperparameter, we got Logistic regression as the best suited model with accuracy 77% with the given datasets. We worked on algorithm based supervised ML technique to get the expected result. Although multiple studies were conducted earlier along the same lines, none of them performed comparative study among different ML techniques or hyperparameter tuning to optimize the results. Besides, this study has been done on the dataset of the most recent COVID-19 pandemic situation, which is itself unique. We captured Twitter data for a duration of 45 days with hashtag #COVID19India OR #COVID19 and analyzed the data using Logistic Regression to find out how the emotion changed over time based on certain social factors
    Źródło:
    Multiple Criteria Decision Making; 2020, 15; 23-35
    2084-1531
    Pojawia się w:
    Multiple Criteria Decision Making
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Support Vector Machine based Decoding Algorithm for BCH Codes
    Autorzy:
    Sudharsan, V.
    Yamuna, B.
    Powiązania:
    https://bibliotekanauki.pl/articles/958048.pdf
    Data publikacji:
    2016
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    BCH codes
    Chase-2 algorithm
    coding gain
    kernel
    multi-class classification
    Soft Decision Decoding
    Support Vector Machine
    Opis:
    Modern communication systems require robust, adaptable and high performance decoders for efficient data transmission. Support Vector Machine (SVM) is a margin based classification and regression technique. In this paper, decoding of Bose Chaudhuri Hocquenghem codes has been approached as a multi-class classification problem using SVM. In conventional decoding algorithms, the procedure for decoding is usually fixed irrespective of the SNR environment in which the transmission takes place, but SVM being a machinelearning algorithm is adaptable to the communication environment. Since the construction of SVM decoder model uses the training data set, application specific decoders can be designed by choosing the training size efficiently. With the soft margin width in SVM being controlled by an equation, which has been formulated as a quadratic programming problem, there are no local minima issues in SVM and is robust to outliers.
    Źródło:
    Journal of Telecommunications and Information Technology; 2016, 2; 108-112
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming
    Autorzy:
    Arif, Salah
    Djellal, Adel
    Djebbari, Nawel
    Belhaoues, Saber
    Touati, Hassen
    Guellati, Fatma Zohra
    Bensouilah, Mourad
    Powiązania:
    https://bibliotekanauki.pl/articles/2174666.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    microcystis cell density
    Multiple Linear Regression
    Support Vector Machine
    Particle Swarm Optimization
    Genetic Algorithm
    Bird Swarm Algorithm
    Opis:
    Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area
    Zastosowanie metody Support Vector Machine w analizie stanu technicznego za budowy terenu górniczego LGOM
    Autorzy:
    Rusek, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/1365604.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    Support Vector Machine
    influence of mining
    structure resistance
    technical wear
    technical condition
    wpływy górnicze
    odporność budynku
    zużycie techniczne
    stan techniczny
    Opis:
    The paper presents the results of the analysis of technical wear of buildings located within impact of mining plant in the Legnica - Głogów Copper District ( LGOM ). The study used method related to neural networks, support vector (Support Vector Machine) in regression approach ε-SVR (Support Vector Regression). The aim of the study was to assess the impact of variables describing the structural protection and renovations on the course modeled phenomenon. The basis for the analysis was created model of technical wear of buildings in the form of a network ε-SVR. In addition to the variables determining the level of structural protection and renovations in the model included variables describing: terrain deformation, mining intensity tremors and the age of the buildings. The choice of model parameters were performed using, as gradientlessness optimization method, genetic algorithm. Based on the established model ε-SVR two types of sensitivity analysis were applied. Assessing the impact of the structural protections have been studying by the analysis of variability of the gradient vector for the modeled hypersurface. The analysis of the impact of renovations on the course modeled process was carried out based on the comparator simulation results of ε-SVR model. The results confirmed the usefulness of the methodology of research and allowed to draw important conclusions on the impact of analyzed factors on the technical wear traditional buildings LGOM.
    W pracy przedstawiono wyniki analizy zużycia technicznego budynków zlokalizowanych w zasięgu wpływów eksploatacji górniczej na terenie Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). W badaniach zastosowano pokrewną sieciom neuronowym metodę wektorów podpierających (Support Vector Machine) w podejściu regresyjnym ε-SVR (Support Vector Regression). Celem badań było uzyskanie oceny wpływu zmiennych opisujących zabezpieczenia konstrukcyjne i remonty na przebieg modelowanego zjawiska. Podstawą do analiz był utworzony model zużycia technicznego budynków w postaci sieci ε-SVR. Oprócz zmiennych określających poziom zabezpieczeń konstrukcyjnych i remontów, w modelu uwzględniono zmienne opisujące: deformacje terenu pochodzenia górniczego, intensywność wstrząsów oraz wiek budynków. Dobór parametrów modelu przeprowadzono z wykorzystaniem, jako bezgradientowej metody optymalizacyjnej, algorytmu genetycznego. Bazując na utworzonym modelu ε-SVR przeprowadzono dwurodzajową analizę wrażliwości. Oceny wpływu zabezpieczeń konstrukcyjnych dokonano badając zmienność wektora gradientu modelowanej hiperpowierzchni. Natomiast analiza wpływu remontów na przebieg modelowanego procesu została przeprowadzona na bazie komparacji wyników symulacji modeluε-SVR. Wyniki badań potwierdziły przydatność przyjętej metodyki badań oraz pozwoliły na sformułowanie istotnych wniosków dotyczących wpływu analizowanych czynników na zużycie techniczne tradycyjnej zabudowy LGOM.
    Źródło:
    Eksploatacja i Niezawodność; 2017, 19, 1; 54-61
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Estimating the compressive strength of concrete, using vacuum dewatering technique
    Autorzy:
    Subhash, D.
    Gupta, S. M.
    Setia, S.
    Pavlykivskyi, V.
    Powiązania:
    https://bibliotekanauki.pl/articles/378711.pdf
    Data publikacji:
    2019
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    vacuum dewatering
    concrete compressive strength
    artificial neural network
    Support Vector Machine
    odwadnianie próżniowe
    wytrzymałość betonu na ściskanie
    sztuczna sieć neuronowa
    maszyna wektorów wspierających
    Opis:
    Purpose: Investigate the potential of vacuum dewatering process of on three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. Design/methodology/approach: For this study a data set of 90 experimental observations obtained from laboratory testing with and without application of vacuum dewatering after designing and casting the concrete of said three grades. The standard cubes of size 150 mm × 150 mm × 150 mm were obtained by core cutting and tested for compression after 3, 7, 14, 21 and 28 days of proper curing. Accuracy of prediction of compressive strength of concrete by application of M5P, ANN and SVM as artificial intelligence techniques and their feasibility are assessed to estimate the compressive strength of the concrete enacted with vacuum dewatering technique. A total data set was segregated in two groups. A group of 63 observations was used for model development and smaller group of 27 observations was used for testing the models. Findings: Overall performance of ANN based developed model is better than M5P and SVM based models for predicting the compressive strength of concrete for this data set. Research limitations/implications: Investigated three different grades of concrete namely M20, M30 and M40 to evaluate its compressive strength. The experimental research involved only testing of cubes only. Practical implications: Using ANN based developed model makes it possible to quickly and accurately predict the compressive strength of concrete. Originality/value: The results of comparing three models for predicting the compressive strength of concrete and the optimal values of ANN based developed models are presented. Earlier no one has applied M5P, ANN and SVM modelling to predict the compressive strength of vacuum dewatered concrete.
    Źródło:
    Archives of Materials Science and Engineering; 2019, 99, 1/2; 30-41
    1897-2764
    Pojawia się w:
    Archives of Materials Science and Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of the influence of mining impacts on the intensity of damage to masonry building structures
    Analiza wpływu oddziaływań górniczych na intensywność uszkodzeń budynków murowanych
    Autorzy:
    Firek, K.
    Powiązania:
    https://bibliotekanauki.pl/articles/105166.pdf
    Data publikacji:
    2017
    Wydawca:
    Politechnika Rzeszowska im. Ignacego Łukasiewicza. Oficyna Wydawnicza
    Tematy:
    technical condition
    buildings
    masonry structure
    mining impacts
    Partial Least Squares Regression
    multiple regression analysis
    Support Vector Machine
    stan techniczny
    budynek
    konstrukcja murowana
    wpływy górnicze
    metoda cząstkowych najmniejszych kwadratów
    PLSR
    analiza regresji wielorakiej
    metoda wektorów podpierających
    SVM
    Opis:
    The paper presents the results of the analysis of the extent of damage to building structures subjected to mining impacts in the form of tremors and continuous surface deformation. The two methods which were used included the multiple regression analysis and the Support Vector Machine – SVM, which belongs to the socalled Machine Learning. The study used the database of the design, technical condition and potential causes of damage to 199 non-renovated buildings, up to the age of 20 years, of a traditional brick construction, located in the mining area of Legnica-Głogów Copper District (LGOM). The conducted analysis allowed for the qualitative assessment of the influence of mining impacts on the extent of damage to the studied buildings.
    W referacie przedstawiono wyniki analizy zakresu uszkodzeń budynków poddanych oddziaływaniom górniczym w postaci wstrząsów oraz ciągłych deformacji terenu. Posłużono się statystyczną metodą regresji wielorakiej oraz metodą wektorów podpierających (Support Vector Machine – SVM) zaliczaną do tzw. uczenia maszynowego (Machine Learning). W badaniach wykorzystano bazę danych o konstrukcji, stanie technicznym i potencjalnych przyczynach uszkodzeń 199 nieremontowanych budynków w wieku do 20 lat, o tradycyjnej konstrukcji murowanej, usytuowanych na terenie górniczym Legnicko-Głogowskiego Okręgu Miedziowego (LGOM). Przeprowadzona analiza pozwoliła na jakościową ocenę wpływu oddziaływań górniczych na zakres uszkodzeń badanych budynków.
    Źródło:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury; 2017, 64, 1; 69-79
    2300-5130
    2300-8903
    Pojawia się w:
    Czasopismo Inżynierii Lądowej, Środowiska i Architektury
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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