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Wyświetlanie 1-15 z 63
Tytuł:
Parallel PBIL applied to power system controller design
Autorzy:
Folly, K.
Powiązania:
https://bibliotekanauki.pl/articles/91747.pdf
Data publikacji:
2013
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
Population-Based Incremental Learning algorithm
PBIL algorithm
Opis:
Population-Based Incremental Learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning derived from artificial neural networks. PBIL has recently received increasing attention in various engineering fields due to its effectiveness, easy implementation and robustness. Despite these strengths, it was reported in the last few years that PBIL suffers from issues of loss of diversity in the population. To deal with this shortcoming, this paper uses parallel PBIL based on multi-population. In parallel PBIL, two populations are used where both probability vectors (PVs) are initialized to 0.5. It is believed that by introducing two populations, the diversity in the population can be increased and better results can be obtained. The approach is applied to power system controller design. Simulations results show that the parallel PBIL approach performs better than the standard PBIL and is as effective as another diversity increasing PBIL called adaptive PBIL.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 3; 215-223
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Wykorzystanie algorytmów samouczących się do sterowania procesem kompostowania biomasy pochodzenia rolniczego
Using self-learning algorithms to control composting process for biomass of agricultural origin
Autorzy:
Neugebauer, M.
Sołowiej, P.
Powiązania:
https://bibliotekanauki.pl/articles/287097.pdf
Data publikacji:
2009
Wydawca:
Polskie Towarzystwo Inżynierii Rolniczej
Tematy:
kompostowanie
sterowanie
algorytm samouczący
composting
control
self-learning algorithm
Opis:
W pracy opisano zbudowane stanowisko do kompostowania materiału biologicznego wraz z odbiorem ciepła. Przedstawiono ideę i wykonanie układu regulacji wykorzystujący algorytm samouczące się sterowania procesem napowietrzania i odbioru ciepła ze stanowiska do kompostowania. Przeprowadzono badania układu regulacji dla różnych punktów początkowych startu procesu (wartości napowietrzania i odbioru ciepła). Uzyskane wyniki badań pokazują, że proces kompostowania przebiegał najkrócej w przypadku rozpoczęcia procesu nauki od zerowych wartości początkowych napowietrzania i odbioru ciepła oraz nie przekroczył temperatury optymalnej dla procesu kompostowania.
The article describes a station designed for biological material composting, including heat reception. The work presents a concept and execution of an adjustment system using a self-learning algorithm for controlling the process involving aeration and heat reception from composting station. The researchers carried out a research on the adjustment system for different initial points of process start (aeration and heat reception values). Obtained research results showed that the composting process was shortest in case of learning process start from initial values of zero aeration and heat reception, and it did not exceed optimal temperature for the composting process.
Źródło:
Inżynieria Rolnicza; 2009, R. 13, nr 8, 8; 147-153
1429-7264
Pojawia się w:
Inżynieria Rolnicza
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-improving Q-learning based controller for a class of dynamical processes
Autorzy:
Musial, Jakub
Stebel, Krzysztof
Czeczot, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/1845515.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
process control
Q-learning algorithm
reinforcement learning
intelligent control
on-line learning
Opis:
This paper presents how Q-learning algorithm can be applied as a general-purpose self-improving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
Źródło:
Archives of Control Sciences; 2021, 31, 3; 527-551
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-improving Q-learning based controller for a class of dynamical processes
Autorzy:
Musial, Jakub
Stebel, Krzysztof
Czeczot, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/1845530.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
process control
Q-learning algorithm
reinforcement learning
intelligent control
on-line learning
Opis:
This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
Źródło:
Archives of Control Sciences; 2021, 31, 3; 527-551
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Model-building adaptive critics for semi-Markov control
Autorzy:
Gosavi, A.
Murray, S.
Hu, J.
Ghosh, S.
Powiązania:
https://bibliotekanauki.pl/articles/91878.pdf
Data publikacji:
2012
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
adaptive critics
learning algorithm
semi-Markov process
decision process
Opis:
Adaptive (or actor) critics are a class of reinforcement learning algorithms. Generally, in adaptive critics, one starts with randomized policies and gradually updates the probability of selecting actions until a deterministic policy is obtained. Classically, these algorithms have been studied for Markov decision processes under model-free updates. Algorithms that build the model are often more stable and require less training in comparison to their model-free counterparts. We propose a new model-building adaptive critic, which builds the model during the learning, for a discounted-reward semi-Markov decision process under some assumptions on the structure of the process. We illustrate the use of our algorithm with numerical results on a system with 10 states and a real-world case-study from management science.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2012, 2, 1; 43-58
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving Crop Yield Predictions in Morocco Using Machine Learning Algorithms
Autorzy:
Ed-Daoudi, Rachid
Alaoui, Altaf
Ettaki, Badia
Zerouaoui, Jamal
Powiązania:
https://bibliotekanauki.pl/articles/24202898.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
crop yield prediction
machine learning algorithm
statistical model
model evaluation
Opis:
In Morocco, agriculture is an important sector that contributes to the country’s economy and food security. Accurately predicting crop yields is crucial for farmers, policy makers, and other stakeholders to make informed decisions regarding resource allocation and food security. This paper investigates the potential of Machine Learning algorithms for improving the accuracy of crop yield predictions in Morocco. The study examines various factors that affect crop yields, including weather patterns, soil moisture levels, and rainfall, and how these factors can be incorporated into Machine Learning models. The performance of different algorithms, including Decision Trees, Random Forests, and Neural Networks, is evaluated and compared to traditional statistical models used for crop prediction. The study demonstrated that the Machine Learning algorithms outperformed the Statistical models in predicting crop yields. Specifically, the Machine Learning algorithms achieved mean squared error values between 0.10 and 0.23 and coefficient of determination values ranging from 0.78 to 0.90, while the Statistical models had mean squared error values ranging from 0.16 to 0.24 and coefficient of determination values ranging from 0.76 to 0.84. The Feed Forward Artificial Neural Network algorithm had the lowest mean squared error value (0.10) and the highest R² value (0.90), indicating that it performed the best among the three Machine Learning algorithms. These results suggest that Machine Learning algorithms can significantly improve the accuracy of crop yield predictions in Morocco, potentially leading to improved food security and optimized resource allocation for farmers.
Źródło:
Journal of Ecological Engineering; 2023, 24, 6; 392--400
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks
Autorzy:
Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan
Powiązania:
https://bibliotekanauki.pl/articles/2201329.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Weighted accuracy algorithmic approach in counteracting fake news and disinformation
Algorytmiczne podejście do dokładności ważonej w przeciwdziałaniu fałszywym informacjom i dezinformacji
Autorzy:
Bonsu, K.O.
Powiązania:
https://bibliotekanauki.pl/articles/2048986.pdf
Data publikacji:
2021
Wydawca:
Akademia Bialska Nauk Stosowanych im. Jana Pawła II w Białej Podlaskiej
Tematy:
artificial intelligence
natural language processing
machine learning algorithm
disinformation
digital revolution
fake news
Opis:
Subject and purpose of work: Fake news and disinformation are polluting information environment. Hence, this paper proposes a methodology for fake news detection through the combined weighted accuracies of seven machine learning algorithms. Materials and methods: This paper uses natural language processing to analyze the text content of a list of news samples and then predicts whether they are FAKE or REAL. Results: Weighted accuracy algorithmic approach has been shown to reduce overfitting. It was revealed that the individual performance of the different algorithms improved after the data was extracted from the news outlet websites and 'quality' data was filtered by the constraint mechanism developed in the experiment. Conclusions: This model is different from the existing mechanisms in the sense that it automates the algorithm selection process and at the same time takes into account the performance of all the algorithms used, including the less performing ones, thereby increasing the mean accuracy of all the algorithm accuracies.
Źródło:
Economic and Regional Studies; 2021, 14, 1; 99-107
2083-3725
2451-182X
Pojawia się w:
Economic and Regional Studies
Dostawca treści:
Biblioteka Nauki
Artykuł
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:
Local Levenberg-Marquardt algorithm for learning feedforwad neural networks
Autorzy:
Bilski, Jarosław
Kowalczyk, Bartosz
Marchlewska, Alina
Zurada, Jacek M.
Powiązania:
https://bibliotekanauki.pl/articles/1837415.pdf
Data publikacji:
2020
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
feed-forward neural network
neural network learning algorithm
optimization problem
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation
Opis:
This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco
Autorzy:
El Azhari, Kaoutar
Abdallaoui, Badreddine
Dehbi, Ali
Abdalloui, Abdelaziz
Zineddine, Hamid
Powiązania:
https://bibliotekanauki.pl/articles/2174362.pdf
Data publikacji:
2022
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
artificial neural network
ANN
learning algorithm
multi-layer perceptron
MLP
modelling
Rabat-Kenitra
relative humidity
Opis:
This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.
Źródło:
Journal of Water and Land Development; 2022, 54; 13--20
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of multi-step algorithms for cognitive maps learning
Autorzy:
Jastriebow, A.
Poczęta, K.
Powiązania:
https://bibliotekanauki.pl/articles/201551.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
cognitive maps
multistep learning algorithm
gradient method
Hebbian algorithm
mapy poznawcze
wieloetapowe nauki algorytmu
metoda gradientu
algorytm kWTA
Opis:
This article is devoted to the analysis of multi-step algorithms for cognitive maps learning. Cognitive maps and multi-step supervised learning based on a gradient method and unsupervised one based on the non-linear Hebbian algorithm were described. Comparative analysis of these methods to one-step algorithms, from the point of view of the speed of convergence of a learning algorithm and the influence on the work of the decision systems was performed. Simulation results were done on prepared software tool ISEMK. Obtained results show that implementation of the multi-step technique gives certain possibilities to get quicker values of target relations values and improve the operation of the learned system.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 4; 735-741
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods applied to sea level predictions in the upper part of a tidal estuary
Autorzy:
Guillou, N.
Chapalain, G.
Powiązania:
https://bibliotekanauki.pl/articles/2078822.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
multiple regression model
artificial neural network
multilayer perceptron
regression function
machine learning algorithm
sea level
Opis:
Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.
Źródło:
Oceanologia; 2021, 63, 4; 531-544
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes
Autorzy:
Akhmetov, Bakhytzhan
Lakhno, Valeriy
Oralbekova, Ayaulym
Kaskatayev, Zhanat
Mussayeva, Gulmira
Powiązania:
https://bibliotekanauki.pl/articles/227158.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information intellectual technology of failure detection
functional control
learning matrix
learning algorithm
operator effectiveness criterion
nodes and aggregates
railway transport
Opis:
Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions. As the main way to improve the efficiency of state detection of the nodes and aggregates of RWT, we see the direction of giving the adaptability property for an automated data processing system from various contactless diagnostic information removal systems. The global purpose can be achieved, in particular, through the use of machine learning methods and failure recognition (recognition objects). In order to improve the operational reliability and service life of the main nodes and aggregates of RWT, there are proposed an appropriate model and algorithm of machine learning of the operator control system of nodes and aggregates. It is proposed to use the Shannon normalized entropy measure and the Kullback-Leibler distance information criterion as a criterion of the learning effectiveness of the automated detection system and operator node state control of RWT. The article describes the application of the proposed method on the example of an automatic detection system (ADS) of the state of a traction motor of an electric locomotive. There are given the test data of the model and algorithm in the MATLAB environment.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 3; 491-496
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
CellProfiler and WEKA Tools: Image Analysis for Fish Erythrocytes Shape and Machine Learning Model Algorithm Accuracy Prediction of Dataset
Autorzy:
Talapatra, Soumendra Nath
Chaudhuri, Rupa
Ghosh, Subhasis
Powiązania:
https://bibliotekanauki.pl/articles/1193348.pdf
Data publikacji:
2021
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Automatic image analysis
CellProfiler tool
Fish erythrocytes quantification
Machine learning algorithm
Model classifier accuracy
Shapes measurement
WEKA tool
Opis:
The first part of the study was detected the number of cells and measurement of shape of cells, cytoplasm, and nuclei in an image of Giemsa-stained of fish peripheral erythrocytes by using CellProfiler (CP, version 2.1.0) tool, an image analysis tool. In the second part, it was evaluated machine learning (ML) algorithm models viz. BayesNet (BN), NaiveBayes (NB), logistic regression (LR), Lazy.KStar (K*), decision tree (DT) J48, Random forest (RF) and Random tree (RT) in the WEKA tool (version 3.8.5) for the prediction of the accuracy of the dataset generated from an image. The CP predicts the numbers and individual cellular area shape (arbitrary unit) of cells, cytoplasm, and nuclei as primary, secondary, and tertiary object data in an image. The performance of model accuracy of studied ML algorithm classifications as per correctly and incorrectly classified instances, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB as per training and testing set of correctly classified instances. In case of performance accuracy of class for K value, the highest values were observed in RF and RT followed by K*, LR, BN and DTJ48 and lowest in NB while lowest values were obtained for mean absolute error (MAE) and root mean squared error (RMSE) in case of RT followed by RF, K*, LR, BN and DTJ48 and comparatively highest value in case of NB as per training and testing set. In conclusion, both tools performed well as an image to the dataset and obtained dataset to rich information through ML modelling and future study in WEKA tool can easily be analysed many biological big data to predict classifier accuracy.
Źródło:
World Scientific News; 2021, 154; 101-116
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
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