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Tytuł:
Assessment of the technical condition of tyres used in agricultural and forestry machinery
Ocena stanu technicznego ogumienia wykorzystywanego w maszynach rolniczych i leśnych
Autorzy:
Gorzelańczyk, P.
Rochowiak, Ł.
Powiązania:
https://bibliotekanauki.pl/articles/2080615.pdf
Data publikacji:
2019
Wydawca:
Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
Tematy:
technical condition
tyre
agricultural machine
forest machine
Opis:
The article addresses the issues related to the testing of properties and assessment of the impact of factors forcing the replacement of tires in agricultural and forestry machines. This is a significant problem regarding the conditions of operation and use of agricultural and forestry machinery, which affects the withdrawal of tires from further use. The first part of the article presents the characteristics of tires for agricultural and forestry machines, presenting their structure, advantages and disadvantages, and the most common damage. In the second and at the same time the main part of the article, the tires of agricultural and forestry machines were examined, and the results and analysis of the conducted measurement tests were presented. The work ends with conclusions that appeared during the tests and proposed solutions for users of the tested tires.
Źródło:
Nauki Inżynierskie i Technologie; 2019, 2(33); 60-70
2449-9773
2080-5985
Pojawia się w:
Nauki Inżynierskie i Technologie
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Ocenka ehkspluatacionnykh svojjstv kolesnogo trelovechnogo traktora
Exploitation properties valuation of wheel tralling tractor
Autorzy:
Bilyk, B.
Borys, M.
Mokhov, S.
Simanovich, V.
Powiązania:
https://bibliotekanauki.pl/articles/77673.pdf
Data publikacji:
2013
Wydawca:
Komisja Motoryzacji i Energetyki Rolnictwa
Tematy:
transmission
dynamic process
dynamic load
work condition
forest machine
wheel tractor
computer programme
fuel economy
mathematical modelling
Opis:
The analysis of the work conditions of forest tractors and machines at their base, and the impact of these conditions on the performance properties of tractors. Calculated scheme of dynamic model of transmission biaxial wheel tractor and computer program has been developed for the tractor movement simulation and dynamic processes in the transmission that allows you to explore the influence of weight and geometric parameters, engine power and the transmission gear number on its speed properties, coefficients of dynamic and fuel economy. It was presented the results of mathematical modeling of the dispersal from the place and dynamic loadings in the transmission of a wheel tralling machine. The dependences of the dynamics coefficients, of the determination of the fuel’s economy and speed property on gear numbers of the transmission’s aggregates was received. It was substantiated the rational significances of the gear numbers of distribute box of a wood tractor, that will reduce dynamic moments, and therefore increase the work durability of the wheel forest transport machine.
Źródło:
Motrol. Motoryzacja i Energetyka Rolnictwa; 2013, 15, 4
1730-8658
Pojawia się w:
Motrol. Motoryzacja i Energetyka Rolnictwa
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analiza porownawcza wybranych wlasciwosci olejow smarujacych uklad tnacy pilarki lancuchowej
Autorzy:
Wojtkowiak, R
Tomczak, R.J.
Powiązania:
https://bibliotekanauki.pl/articles/832882.pdf
Data publikacji:
2003
Wydawca:
Instytut Hodowli i Aklimatyzacji Roślin
Tematy:
dane techniczne
uklady tnace
maszyny lesne
wlasciwosci
olej rzepakowy
pilarki lancuchowe
oleje smarowe
smarowanie
technical data
cutting system
forest machine
property
rapeseed oil
chain saw
lubricating oil
lubrication
Źródło:
Rośliny Oleiste - Oilseed Crops; 2003, 24, 1; 317-325
1233-8273
Pojawia się w:
Rośliny Oleiste - Oilseed Crops
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Interpretative machine learning as a key in recognizing the variability of lakes trophy patterns
Autorzy:
Jasiewicz, Jarosław
Zawiska, Izabela
Rzodkiewicz, Monika
Woszczyk, Michał
Powiązania:
https://bibliotekanauki.pl/articles/2054583.pdf
Data publikacji:
2022-03-31
Wydawca:
Uniwersytet im. Adama Mickiewicza w Poznaniu
Tematy:
total phosphorus
interpretative machine learning
random forest
Masurian lakes
Opis:
The paper presents an application of interpretative machine learning to identify groups of lakes not with similar features but with similar potential factors influencing the content of total phosphorus – Ptot. The method was developed on a sample of 60 lakes from North-Eastern Poland and used 25 external explanatory variables. Selected variables are stable over a long time, first group includes morphometric parameters of lakes and the second group en- compass watershed geometry geology and land use. Our method involves building a regression model, creating an ex- plainer, finding a set of mapping functions describing how each variable influences the outcome, and finally clustering objects by ’the influence’. The influence is a non-linear and non-parametric transformation of the explanatory variables into a form describing a given variable impact on the modeled feature. Such a transformation makes group data on the functional relations between the explanatory variables and the explained variable possible. The study reveals that there are five clusters where the concentration of Ptot is shaped similarly. We compared our method with other numerical analyses and showed that it provides new information on the catchment area and lake trophy relationship.
Źródło:
Quaestiones Geographicae; 2022, 41, 1; 127-146
0137-477X
2081-6383
Pojawia się w:
Quaestiones Geographicae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predicting immunogenicity in murine hosts with use of Random Forest classifier
Przewidywanie immunogenności u myszy przy użyciu klasyfikatora Random Forest
Autorzy:
Marciniak, Anna
Tarczewska, Martyna
Kloska, Sylwester
Powiązania:
https://bibliotekanauki.pl/articles/2016293.pdf
Data publikacji:
2020
Wydawca:
Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB
Tematy:
Random Forest Classifier
immunogenicity
machine learning
entropy
Gini index
klasyfikator Random Forest
immunogenność
uczenie maszynowe
entropia
Opis:
Biomedical data are difficult to interpret due to their large amount. One of the solutions to cope with this problem is to use machine learning. Machine learning can be used to capture previously unnoticed dependencies. The authors performed random forest classifier with entropy and Gini index criteria on immunogenicity data. Input data consisted of 3 columns: epitope (8-11 amino acids long peptide), major histocompatibility complex (MHC) and immune response. Presented model can predict the immune response based on epitope-MHC complex. Achieved results had accuracy of 84% for entropy and 83% for Gini index. The results are not fully satisfying but are a fair start for more complexed experiments and could be used as an indicator for further research.
Dane biomedyczne są trudne do interpretacji ze względu na ich dużą ilość. Jednym z rozwiązań radzenia sobie z tym problemem jest wykorzystanie uczenia maszynowego. Techniki te umożliwiają wychwycenie wcześniej niezauważonych zależności. W artykule przedstawiono wykorzystanie klasyfikatora Random Forest z kryterium entropii i indeksem Gini na danych dotyczących immunogenności. Dane wejściowe składają się z 3 kolumn: epitop (peptyd o długości 8-11 aminokwasów), główny kompleks zgodności tkankowej (MHC) i odpowiedź immunologiczna. Zaprezentowany model przewiduje odpowiedź immunologiczną na podstawie kompleksu epitop-MHC. Uzyskane wyniki osiągnęły dokładność na poziomie 84% (entropia) i 83% (indeks Gini). Wyniki nie są w pełni satysfakcjonujące, ale stanowią dobry początek dla bardziej złożonych eksperymentów i wyznacznik do dalszych badań.
Źródło:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2020, 24; 31-43
1899-0088
Pojawia się w:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Artificial Intelligence Based Flood Forecasting for River Hunza at Danyor Station in Pakistan
Autorzy:
Yaseen, Muhammad Waseem
Awais, Muhammad
Riaz, Khuram
Rasheed, Muhammad Babar
Waqar, Muhammad
Rasheed, Sajid
Powiązania:
https://bibliotekanauki.pl/articles/31340346.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Instytut Budownictwa Wodnego PAN
Tematy:
hydrometeorology
random forest
support vector
multilayer perceptron
machine learning
flood forecasting
Opis:
Floods can cause significant problems for humans and can damage the economy. Implementing a reliable flood monitoring warning system in risk areas can help to reduce the negative impacts of these natural disasters. Artificial intelligence algorithms and statistical approaches are employed by researchers to enhance flood forecasting. In this study, a dataset was created using unique features measured by sensors along the Hunza River in Pakistan over the past 31 years. The dataset was used for classification and regression problems. Two types of machine learning algorithms were tested for classification: classical algorithms (Random Forest, RF and Support Vector Classifier, SVC) and deep learning algorithms (Multi-Layer Perceptron, MLP). For the regression problem, the result of MLP and Support Vector Regression (SVR) algorithms were compared based on their mean square, root mean square and mean absolute errors. The results obtained show that the accuracy of the RF classifier is 0.99, while the accuracies of the SVC and MLP methods are 0.98; moreover, in the case of flood prediction, the SVR algorithm outperforms the MLP approach.
Źródło:
Archives of Hydro-Engineering and Environmental Mechanics; 2022, 69, 1; 59-77
1231-3726
Pojawia się w:
Archives of Hydro-Engineering and Environmental Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers
Autorzy:
Saini, Rashmi
Powiązania:
https://bibliotekanauki.pl/articles/2174656.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
Machine Learning
Random Forest
AdaBoost
vegetation mapping
vegetation indices
Opis:
Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.
Źródło:
Geomatics and Environmental Engineering; 2023, 17, 1; 57--74
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A random forest model for the prediction of spudcan penetration resistance in stiff-over-soft clays
Autorzy:
Gao, Pan
Liu, Zhihui
Zeng, Ji
Zhan, Yiting
Wang, Fei
Powiązania:
https://bibliotekanauki.pl/articles/1573798.pdf
Data publikacji:
2020
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
machine learning
random forest
jack-up
penetration resistance
stiff-over-soft clays
Opis:
Punch-through is a major threat to the jack-up unit, especially at well sites with layered stiff-over-soft clays. A model is proposed to predict the spudcan penetration resistance in stiff-over-soft clays, based on the random forest (RF) method. The RF model was trained and tested with numerical simulation results obtained through the Finite Element model, implemented with the Coupled Eulerian Lagrangian (CEL) approach. With the proposed CEL model, the effects of the stiff layer thickness, undrained shear strength ratio, and the undrained shear strength of the soft layer on the bearing characteristics, as well as the soil failure mechanism, were numerically studied. A simplified resistance profile model of penetration in stiff-over-soft clays is proposed, divided into three sections by the peak point and the transition point. The importance of soil parameters to the penetration resistance was analysed. Then, the trained RF model was tested against the test set, showing a good prediction of the numerical cases. Finally, the trained RF was validated against centrifuge tests. The RF model successfully captured the punch-through potential, and was verified using data recorded in the field, showing advantages over the SNAME guideline. It is supposed that the trained RF model should give a good prediction of the spudcan penetration resistance profile, especially if trained with more field data.
Źródło:
Polish Maritime Research; 2020, 4; 130-138
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of machine learning algorithms to predict permeability in tight sandstone formations
Zastosowanie metod uczenia maszynowego do przewidywania przepuszczalności w formacjach zwięzłych piaskowców typu tight gas
Autorzy:
Topór, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2143653.pdf
Data publikacji:
2021
Wydawca:
Instytut Nafty i Gazu - Państwowy Instytut Badawczy
Tematy:
machine learning
random forest
permeability
prediction
uczenie maszynowe
lasy losowe
predykcja
przepuszczalność
Opis:
The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.
Zastosowanie algorytmów uczenia maszynowego w geologii naftowej otworzyło nowy rozdział w poszukiwaniu złóż ropy i gazu. Algorytmy uczenia maszynowego zostały z powodzeniem wykorzystane do przewidywania kluczowych właściwości petrofizycznych charakteryzujących złoże. W pracy zastosowano metody uczenia maszynowego do przewidywania przepuszczalności w warunkach ustalonego ciśnienia złożowego dla formacji zwięzłych piaskowców typu tight gas. Modele zostały skonstruowane przy użyciu algorytmów o różnym stopniu komplikacji (wielowymiarowa regresja liniowa – MLR i lasy losowe – RF), a następnie poddano je procesowi uczenia na danych zawierających podstawowe informacje o otworze, podstawowe parametry petrofizyczne oraz typ skał pochodzący z makroskopowego i mikroskopowego opisu próbek rdzeni. Typ skał został rozkodowany i poddany procesowi inżynierii cech, aby wydobyć dodatkowe zmienne do modelu. Proces uczenia na zbiorze treningowym został przeprowadzony z wykorzystaniem 10-krotnej kroswalidacji. Uzyskane wyniki pokazują, że oba algorytmy mogą przewidywać przepuszczalność z dużą dokładnością (R2 = 0,800 dla MLR vs R2 = 0,834 dla RF). Dokładność modelu RF jest około 3% lepsza niż MLR i około 6% lepsza w porównaniu do modelu referencyjnego (model regresji liniowej z jedną zmienną – porowatością). W przypadku obu modeli porowatość była najistotniejszym parametrem przy przewidywaniu przepuszczalności. Dodatkowo w modelu wykorzystującym lasy losowe istotną cechą okazała się głębokość próbki, co może świadczyć o dodatkowych interakcjach pomiędzy zmiennymi. Cechą wspólną próbek w zbiorze treningowym i testowym, dla których modele zadziałały ze słabą skutecznością, były porowatość od 3% do 10% i brak spękań. Dodatkowo w zbiorze testowym niska dokładność przewidywań przepuszczalności była związana z obecnością cementacji kalcytem i kwarcem. Workflow wykorzystujący stan wiedzy dotyczącej modelowania, którego trzon stanowi pakiet tidymodels, będzie dalej stosowany do prognozowania przestrzennych właściwości petrofizycznych na podstawie atrybutów sejsmicznych.
Źródło:
Nafta-Gaz; 2021, 77, 5; 283-292
0867-8871
Pojawia się w:
Nafta-Gaz
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Developing a data-driven soft sensor to predict silicate impurity in iron ore flotation concentrate
Autorzy:
Pural, Yusuf Enes
Powiązania:
https://bibliotekanauki.pl/articles/24148677.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
soft sensor
machine learning
random forest
multi-layer perceptron
flotation
grade estimation
Opis:
Soft sensors are mathematical models that estimate the value of a process variable that is difficult or expensive to measure directly. They can be based on first principle models, data-based models, or a combination of both. These models are increasingly used in mineral processing to estimate and optimize important performance parameters such as mill load, mineral grades, and particle size. This study investigates the development of a data-driven soft sensor to predict the silicate content in iron ore reverse flotation concentrate, a crucial indicator of plant performance. The proposed soft sensor model employs a dataset obtained from Kaggle, which includes measurements of iron and silicate content in the feed to the plant, reagent dosages, weight and pH of pulp, as well as the amount of air and froth levels in the flotation units. To reduce the dimensionality of the dataset, Principal Component Analysis, an unsupervised machine learning method, was applied. The soft sensor model was developed using three machine learning algorithms, namely, Ridge Regression, Multi-Layer Perceptron, and Random Forest. The Random Forest model, created with non-reduced data, demonstrated superior performance, with an R-squared value of 96.5% and a mean absolute error of 0.089. The results suggest that the proposed soft sensor model can accurately predict the silicate content in the iron ore flotation concentrate using machine learning algorithms. Moreover, the study highlights the importance of selecting appropriate algorithms for soft sensor developments in mineral processing plants.
Źródło:
Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 169823
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method
Autorzy:
Choi, Young-Sin
Kwon, Do-Hun
Lee, Min_Woo
Cha, Eun-Ji
Jeon, Junhyub
Lee, Seok-Jae
Kim, Jongryoul
Kim, Hwi-Jun
Powiązania:
https://bibliotekanauki.pl/articles/2174571.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Fe-based amorphous
soft magnetic properties
artificial intelligence
machine learning
random forest regression
Opis:
The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe80.5Si3.63B13.54C2.33 at.% and the highest saturation magnetization was obtained Fe81.83Si3.63B12.63C1.91at.% with R2 values of 0.74 and 0.878, respectively.
Źródło:
Archives of Metallurgy and Materials; 2022, 67, 4; 1459--1463
1733-3490
Pojawia się w:
Archives of Metallurgy and Materials
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose
Autorzy:
Osowski, S.
Siwek, K.
Powiązania:
https://bibliotekanauki.pl/articles/220792.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
data mining
electronic nose
gasoline blends
random forest
support vector machine
wavelet denoising
Opis:
The paper analyses the distorted data of an electronic nose in recognizing the gasoline bio-based additives. Different tools of data mining, such as the methods of data clustering, principal component analysis, wavelet transformation, support vector machine and random forest of decision trees are applied. A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals. A special denoising procedure based on application of discrete wavelet transformation has been proposed. This procedure enables to reduce the error rate of recognition in a significant way. The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.
Źródło:
Metrology and Measurement Systems; 2017, 24, 1; 27-44
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images
Autorzy:
Taha, Lamyaa Gamal El-deen
Ibrahim, Rania Elsayed
Powiązania:
https://bibliotekanauki.pl/articles/1837996.pdf
Data publikacji:
2021
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
ensemble classifiers
machine learning
random forest
maximum likelihood
support vector machines
backpropagation
image classification
Opis:
The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
Źródło:
Geomatics and Environmental Engineering; 2021, 15, 4; 101-116
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Semantic Segmentation of Diseases in Mushrooms using Enhanced Random Forest
Autorzy:
Yacharam, Rakesh Kumar
Sekhar, Dr. V. Chandra
Powiązania:
https://bibliotekanauki.pl/articles/31339414.pdf
Data publikacji:
2023
Wydawca:
Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej
Tematy:
mushroom diseases
semantic segmentation
computer aided
Machine Learning
significant feature extraction
Random Forest classifier
Opis:
Mushrooms are a rich source of antioxidants and nutritional values. Edible mushrooms, however, are susceptible to various diseases such as dry bubble, wet bubble, cobweb, bacterial blotches, and mites. Farmers face significant production losses due to these diseases affecting mushrooms. The manual detection of these diseases relies on expertise, knowledge of diseases, and human effort. Therefore, there is a need for computer-aided methods, which serve as optimal substitutes for detecting and segmenting diseases. In this paper, we propose a semantic segmentation approach based on the Random Forest machine learning technique for the detection and segmentation of mushroom diseases. Our focus lies in extracting a combination of different features, including Gabor, Bouda, Kayyali, Gaussian, Canny edge, Roberts, Sobel, Scharr, Prewitt, Median, and Variance. We employ constant mean-variance thresholding and the Pearson correlation coefficient to extract significant features, aiming to enhance computational speed and reduce complexity in training the Random Forest classifier. Our results indicate that semantic segmentation based on Random Forest outperforms other methods such as Support Vector Machine (SVM), Naïve Bayes, K-means, and Region of Interest in terms of accuracy. Additionally, it exhibits superior precision, recall, and F1 score compared to SVM. It is worth noting that deep learning-based semantic segmentation methods were not considered due to the limited availability of diseased mushroom images.
Źródło:
Machine Graphics & Vision; 2023, 32, 2; 129-146
1230-0535
2720-250X
Pojawia się w:
Machine Graphics & Vision
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
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