Wyniki wyszukiwania - BazTech

1

Effects of fruit baggings as preharvest treatments on the fruit quality of pineapple ‘MD-2’

Lestari Ria Rizky, Widodo Soesiladi Esti, Waluyo Sri

Acta Innovations

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2023

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no. 50

41--45

EN

The demand for fresh pineapple fruit is currently highest for the MD2 pineapple variety. Continuous efforts are made to enhance the quality of MD2 pineapples, including the fruit skin color, flesh color, sweetness, and minimizing sunburn damage. Bagging is one of the pre-harvest methods that can be employed for this purpose. This research aims to find suitable bagging materials that meet the industry's criteria and assess the severity of sunburn in each bagging treatment. A completely randomized design was used in this study, with six different bagging materials and pineapples aged 80 Days After Forcing (DAF). The bagging materials used were the control, blue Polyethylene (PE) bag, white PE bag, black paranet bag, paper bag, and the existing cap- shaped bagging technique using recycled paper from banana bagging, as utilized by PT. Great Giant Pineapple. Each treatment involved 120 pineapple samples harvested at 140 DAF. MD2 pineapples without bagging were found to provide the best results according to PT. Great Giant Pineapple's criteria, with green skin color (1.35%) and uniform yellow flesh (85.62%).

2

Snow water equivalent prediction in a mountainous area using hybrid bagging machine learning approaches

Khosravi Khabat, Golkarian Ali, Omidvar Ebrahim, Hatamiafkoueieh Javad, Shirali Masoud

Acta Geophysica

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2023

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Vol. 71, no. 2

1015--1031

EN

Snow Water Equivalent (SWE) is one of the most critical variables in mountainous watersheds and needs to be considered in water resources management plans. As direct measurement of SWE is difficult and empirical equations are highly uncertain, the present study aimed to obtain accurate predictions of SWE using machine learning methods. Five standalone algorithms of tree-based [M5P and random tree (RT)], rule-based [M5Rules (M5R)] and lazy-based learner (IBK and Kstar) and five novel hybrid bagging-based algorithms (BA) with standalone models (i.e., BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) were developed. A total of 2550 snow measurements were collected from 62 snow and rain-gauge stations located in 13 mountainous provinces in Iran. Data including ice beneath the snow (IBS), fresh snow depth (FSD), length of snow sample (LSS), snow density (SDN), snow depth (SD) and time of falling (TS) were measured. Based on the Pearson correlation between inputs (IBS, FSD, LSS, SDN, SD and TS) and output (SWE), six different input combinations were constructed. The dataset was separated into two groups (70% and 30% of the data) by a cross-validation technique for model construction (training dataset) and model evaluation (testing dataset), respectively. Different visual and quantitative metrics (e.g., Nash–Sutclife efficiency (NSE)) were used for evaluating model accuracy. It was found that SD had the highest correlation with SWE in Iran (r=0.73). In general, the bootstrap aggregation (i.e., bagging) hybrid machine learning methods (BA-M5P, BA-RT, BA-IBK, BA-Kstar and BA-M5R) increased prediction accuracy when compared to each standalone method. While BA-M5R had the highest prediction accuracy (NSE=0.83) (considering all six input variables), BA-IBK could predict SWE with high accuracy (NSE=0.71) using only two input variables (SD and LSS). Our findings demonstrate that SWE can be accurately predicted through a variety of machine learning methods using easily measurable variables and may be useful for applications in other mountainous regions across the globe.

3

GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models

Mabdeh Ali Nouh, Al Fugara A’kif, Ahmadlou Mohammad, Al Adamat Rida, Al Shabeeb Abdel Rahman

Acta Geophysica

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2022

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Vol. 70, no 3

1253--1267

EN

Landslides are a geological phenomenon that is causing considerable economic and human losses annually in various regions of the world. In some cases, the complex behaviors of some such phenomena cause that single machine learning models fail in modeling them well. To overcome this issue, this paper presents two novel genetic-algorithm (GA)-based ensemble models constructed with the decision tree (DT), k-nearest neighbors (KNN), and Naive Bayes (NB) models based on the bagging and random sub-space (RS) methods for landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan. Sixteen factors, including topographic, climatic, human, and geological factors were used as possible factors that influence landslide occurrence in the study area. In addition to this, one hundred and ninety two landslide locations were employed for training and testing the models. The GA was used in this study for feature selection based on three models: DT, KNN, and NB. Model performance evaluation based on the area under the receiver operating characteristic (AUROC) curve indicated that the ensemble models outperform the standalone ones. The values of the AUROC curves in the validation phases for the five models, namely, the GA-based DT, KNN, NB, bagging-based, and RS-based ensemble model, were 0.63, 0.69, 0.63, 0.89, and 0.95, respectively. The results of this study suggest that simple models can be combined using the bagging and RS methods to produce integrated models that have higher accuracy than that of any of the individual simple models.

4

Bootstrap Aggregation Technique for Evaluating the Significance of Manufacturing Process Parameters in the Glass Industry

Paśko Łukasz, Kuś Aneta

Technical Sciences / University of Warmia and Mazury in Olsztyn

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2021

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nr 24(1)

135--155

EN

The article presents the application of the bootstrap aggregation technique to create a set of artificial neural networks (multilayer perceptron). The task of the set of neural networks is to predict the number of defective products on the basis of values of manufacturing process parameters, and to determine how the manufacturing process parameters affect the prediction result. For this purpose, four methods of determining the significance of the manufacturing process parameters have been proposed. These methods are based on the analysis of connection weights between neurons and the examination of prediction error generated by neural networks. The proposed methods take into account the fact that not a single neural network is used, but the set of networks. The article presents the research methodology as well as the results obtained for real data that come from a glassworks company and concern a production process of glass packaging. As a result of the research, it was found that it is justified to use a set of neural networks to predict the number of defective products in the glass industry, and besides, the significance of the manufacturing process parameters in the glassworks company was established using the developed set of neural networks.

5

Bagging and boosting techniques in prediction of particulate matters

Triana D., Osowski S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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Vol. 68, nr 5

1207--1215

EN

The paper presents new ensemble solutions, which can forecast the average level of particulate matters PM10 and PM2.5 with increased accuracy. The proposed network is composed of weak predictors integrated into a final expert system. The members of the ensemble are built based on deep multilayer perceptron and decision tree and use bagging and boosting principle in elaborating common decisions. The numerical experiments have been carried out for prediction of daily average pollution of PM10 and PM2.5 for the next day. The results of experiments have shown, that bagging and boosting ensembles employing these weak predictors improve greatly the quality of results. The mean absolute errors have been reduced by more than 30% in the case of PM10 and 20% in the case of PM2.5 in comparison to individually acting predictors.

6

Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers

Biju V. G., Prashanth CM

Journal of Applied Computer Science Methods

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2017

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Vol. 9 No. 1

23--47

EN

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.

7

Application of selected supervised classification methods to bank marketing campaign

Grzonka D., Borowik B., Suchacka G.

Information Systems in Management

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2016

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Vol. 5, No. 1

36--48

EN

Supervised classification covers a number of data mining methods based on training data. These methods have been successfully applied to solve multi-criteria complex classification problems in many domains, including economical issues. In this paper we discuss features of some supervised classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained results are used for evaluating the effectiveness of the classification rules.

8

Computer-aided obstructive sleep apnea screening from single-lead electrocardiogram using statistical and spectral features and bootstrap aggregating

Hassan A. R., Haque M. A.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 1

256--266

EN

Automatic sleep apnea screening is important to alleviate the onus of the physicians of analyzing a large volume of data visually. Again, the push towards low-power, portable and wearable sleep quality monitoring systems necessitates the use of minimum number of recording channels to enhance battery life. So, there is a dire need of an automated apnea detection scheme based on single-lead electrocardiogram (ECG). Most of the existing works are based on multiple channels of physiological signals or yield poor performance. The effect of various classification models on algorithmic performance is also poorly explored. In the present work, we propose a statistical and spectral feature based sleep apnea identification scheme that utilizes single-lead ECG signals. Bootstrap aggregating is employed to perform classification. The efficacy of the selected features is demonstrated by intuitive, statistical and graphical analyses. Optimal choices of classifier parameters are also expounded. The performance of the proposed algorithm is evaluated for various classifiers. The performance of our method is also compared to that of the state-of-the-art ones. The proposed method yields accuracy, sensitivity and specificity of 85.97%, 84.14% and 86.83% respectively on a widely used benchmark data-set. Experimental findings backed by statistical and graphical analyses suggest that the proposed method performs better than the existing ones in terms of accuracy, sensitivity, specificity and computational cost.

9

Evaluation of the Residual Bagging Height using the Regression Technique and Fuzzy Theory

Jaouachi B.

Fibres & Textiles in Eastern Europe

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2013

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Vol. 21, no. 4 (100)

92--98

EN

This paper deals with the evaluation of the residual bagging height using the fuzzy theory method. An experimental design was used to objectively evaluate the effect of input parameters on the residual bagging height of knitted fabrics using two different yarn structures without and within elastane filament. Our findings show that among the overall input parameters tested, two are influential. In our experimental design the fuzzy theory method was applied using five different membership functions: triangular-shaped, trapezoidal function, Gaussian, generalized-bell and Đ-shaped . To prove our results, the experimental regression technique and fuzzy theory were compared. In addition, among the other membership functions tested, the triangular membership function gives a more effective evaluation and widely fits to residual bagging height behaviour.

PL

Praca opisuje ocenę wysokości odkształcenia przy wypychaniu dzianin przy zastosowaniu teorii zbiorów rozmytych. Eksperymentalny system został opracowany do oceny wpływu parametrów wyjściowych. Do badań przyjęto dzianiny wykonane z dwóch rodzajów przędzy, z oraz bez elastomerowego składnika. Nasze wyniki pokazują, że spośród wszystkich badanych parametrów wejściowych 2 posiadają istotne znaczenie. Do badań korzystano z metody zbiorów rozmytych stosując 5 różnych funkcji przynależności: trójkątną, trapezoidalną, Gaussowską, dzwonową oraz typu Pi. Dla oceny uzyskanych wyników przeprowadzono porównanie danych uzyskanych z teorii zbiorów rozmytych oraz techniki regresji danych doświadczalnych. Stwierdzono, że najlepsze rezultaty uzyskano stosując metodę trójkątnej funkcji przynależności, przy której korelacja z danymi eksperymentalnymi wysokości odkształcenia przy wypychaniu była największa.

10

Regression Model for the Bagging Fatigue of Knitted Fabrics Produced from Viscose/Polyester Blended Rotor Yarns

Hasani H., Hassan Zadeh S.

Fibres & Textiles in Eastern Europe

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2012

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Vol. 20, no. 4 (93)

67--71

EN

The aim of this work was to predict the bagging fatigue percentage of knitted fabrics produced from viscose/polyester blended rotor yarns using blend ratios and structural cell stitch lengths as predictor variables. A simplex lattice design was used to determine the combinations of blend ratios of the fibre types. Knitted fabrics with three different structures were produced from viscose/polyester blended rotor yarns. Mixture-process crossed regression models with two mixture components and one process variable (structural cell stitch lengths, blend ratio) were built to predict the bagging fatigue percentage. All statistical analysis steps were implemented using Design-Expert statistical software. The correlation coefficient between the bagging fatigue percentage predicted and the bagging fatigue percentage observed was 0.983, indicating the strong predictive capability of the regression model built.

PL

Badano wypychanie dzianin wykonanych z wiskozowo-poliestrowych mieszankowych przędz rotorowych. Jako wielkości wejściowe przyjęto procentowy udział włókien w mieszankach splotu. Zastosowano konstrukcję sympleksu dla określenia kombinacji stosunku składników przędzy mieszankowej. Wyprodukowano trzy rodzaje dzianin przy użyciu rożnych mieszanek przędz, utworzono model regresji zawierający dwa składniki mieszanki i jedną zmienną procesu - długość splotu dziewiarskiego. Przeprowadzono analizę statystyczną przy wykorzystaniu programu Design-Expert. Uzyskano bardzo dobrą zgodność pomiędzy wartościami przewidywanymi i pomierzonymi, współczynnik korelacji wynosił 0.983.

11

Boosting, bagging and fixed fusion methods performance for aiding diagnosis

Ćwiklińska-Jurkowska M.

Biocybernetics and Biomedical Engineering

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2012

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Vol. 32, no. 2

17-31

EN

Multiple classifier fusion may generate more accurate classification than each of the constituent classifiers. The aim was to examine the ensemble performance by the comparison of boosting, bagging and fixed fusion methods for aiding diagnosis. Real-life medical data set for thyroid diseases recognition was applied. Different fixed combined classifiers (mean, average, product, minimum, maximum, and majority vote) built on parametric and nonparametric Bayesian discriminant methods have been employed. No very significant improvement of recognition rates by a fixed classifier combination was achieved on the examined data. The best performance was obtained for resampling methods with classification trees, for both the bagging and the boosting combining methods. The bagging and the boosting logistic regression methods have proven less efficient than the bagging or the boosting of neural networks. Difference between the bagging and the boosting performance for the examined data set was not obtained.

12

Evolving ensembles of linear classifiers by means of clonal selection algorithm

Bereta M., Burczyński T.

Control and Cybernetics

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2010

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Vol. 39, no 2

325-342

EN

Artificial immune systems (AIS) have become popular among researchers and have been applied to a variety of tasks. Developing supervised learning algorithms based on metaphors from the immune system is still an area in which there is much to explore. In this paper a novel supervised immune algorithm based on clonal selection framework is proposed. It evolves a population of linear classifiers used to construct a set of classification rules. Aggregating strategies, such as bagging and boosting, are shown to work well with the proposed algorithm as the base classifier.