Wyniki wyszukiwania - BazTech

1

Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

Arif Salah, Djellal Adel, Djebbari Nawel, Belhaoues Saber, Touati Hassen, Guellati Fatma Zohra, Bensouilah Mourad

Geomatics and Environmental Engineering

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2023

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Vol. 17, no. 2

31--68

EN

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.

2

Assessing the Shallow Water Habitat Mapping Extracted from High-Resolution Satellite Image with Multi Classification Algorithms

Nandika Muhammad Rizki, Ulfa Azura, Ibrahim Andi, Purwanto Anang Dwi

Geomatics and Environmental Engineering

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2023

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Vol. 17, no. 2

69--87

EN

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.

3

Hybrid feature selection and support vector machine framework for predicting maintenance failures

Tarik Mouna, Mniai Ayoub, Jebari Khalid

Applied Computer Science

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2023

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Vol. 19, no 2

112--124

EN

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.

4

A comparative study on performance of basic and ensemble classifiers with various datasets

Gunakala Archana, Shahid Afzal Hussain

Applied Computer Science

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2023

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Vol. 19, no 1

107--132

EN

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.

5

Application of Support Vector Machine in the analysis of the technical state of development in the LGOM mining area

Rusek J.

Eksploatacja i Niezawodność

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2017

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Vol. 19, no. 1

54--61

EN

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.

PL

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.

6

Linear SVM for organizing data

Jurczak M.

Przegląd Elektrotechniczny

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2006

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R. 82, nr 1

47-49

EN

This paper demonstrates that the text categorization (TC) is a good automatic method for organizing data. Some features of the TC problem are described and explained that linear Support Vector Machines (SVM) is an appropriate technique for this task. Theoretical considerations are illustrated through examples in which the text categorization problem has been solved with SVM.

PL

Artykuł omawia problem kategoryzacji tekstu jako dobrego rozwiązania do automatycznej organizacji danych. Przedstawia cechy problemu TC oraz wyjaśnia, iż liniowa metoda wektorów podtrzymujących (SVM) doskonale sprawdza się w tego typu zadaniach. Teoretyczne rozważania ilustrowane są przykładami automatycznej organizacji dokumentów przy wykorzystaniu sieci typu SVM.