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Tytuł:
A rule based machine learning approach to the nonlinear multifingered robot gripper problem
Autorzy:
Abu-Zitar, R.
Al-Fahed Nuseirat, A. M.
Powiązania:
https://bibliotekanauki.pl/articles/970099.pdf
Data publikacji:
2005
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
zacisk robota
programowanie ewolucyjne
komputerowe uczenie się
robot gripper
nonlinear complementarity problem (NCP)
Evolutionary Programming (EP)
machine learning
nearest-classifier-algorithm
Opis:
In this paper, we present a novel method that utilizes the accumulation of knowledge in a rule base for solving the nonlinear frictional gripper problem for both the isotropic and orthotropic cases. The knowledge is discovered and accumulated in a rule base with the aid of a genetic based machine learning mechanism. This machine learning mechanism extracts rules for solving the problem with the help of the Evolutionary Programming [EP) algorithm. The retrievals are done using the nearest-classifier-algorithm. This approach provides online solutions for the problem, and establishes a dynamic and evolving environment that adapts with new and sudden changes on the grip specifications or on the external forces. The resulting grasping forces using the presented method are compared with grasping forces obtained using other methods, such as the Complementarity Problems. The proposed online method could update the needed grasping forces to keep firm grip if the configuration of the forces externally applied to the object is changed. Numerical examples that illustrate the proposed method are presented.
Źródło:
Control and Cybernetics; 2005, 34, 2; 553-573
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP
Autorzy:
Amer, F. Z.
El-Garhy, A. M.
Awadalla, M. H.
Rashad, S. M.
Abdien, A. K.
Powiązania:
https://bibliotekanauki.pl/articles/147652.pdf
Data publikacji:
2011
Wydawca:
Instytut Chemii i Techniki Jądrowej
Tematy:
support vector machine (SVM)
fault classification
multi fault classification
genetic algorithm (GA)
machine learning
Opis:
Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.
Źródło:
Nukleonika; 2011, 56, 4; 323-332
0029-5922
1508-5791
Pojawia się w:
Nukleonika
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization
Autorzy:
Barszcz, Tomasz
Zabaryłło, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/2052104.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
fault detection
transient
turbine generator
genetic algorithm
Opis:
Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.
Źródło:
Metrology and Measurement Systems; 2022, 29, 1; 175-190
0860-8229
Pojawia się w:
Metrology and Measurement Systems
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:
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:
Building computer vision systems using machine learning algorithms
Autorzy:
Boyko, N.
Sokil, N.
Powiązania:
https://bibliotekanauki.pl/articles/410768.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Oddział w Lublinie PAN
Tematy:
algorithm
information system
neural network
machine learning
client-server architecture
script
artificial system
machine learning algorithms
algorytm
systemy informacyjne
sieci neuronowe
systemy uczące
architektura klient-serwer
skrypt
Opis:
In this paper theoretic aspects of machine learning system in the field of computer vision is considered. There are presented methods of behavior analysis. There are offered tasks and problems associated with building systems using machine learning algorithm. The paper provides signs of problems that can be solved by using machine learning algorithms There is demonstrated step by step construction of computer vision system. The paper provides the algorithm of solving the problem of binary (two classes) classification for demonstration the machine learning algorithm possibilities in image recognition field, which can recognize the gender of the person on the photo. Aspects related to the search of data processing are also considered. There is analyzed the search of optimal parameters for algorithms. An interpretation of results in machine learning algorithm is provided. Binarization methods in machine learning algorithm are offered. There is analyzed the technology for improving the accuracy of machine learning algorithm. There are proposed ways to improve computer vision system in neural systems. Also there are analyzed large software modules that work using machine learning systems. The article provides prospects of powerful information technologies, which are necessary for the proper data selection in learning and configuration of feature extraction algorithm to create a computer vision system.
Źródło:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 15-20
2084-5715
Pojawia się w:
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Autorzy:
Chen, Xuan
Przystupa, Krzysztof
Ye, Zhiwei
Chen, Feng
Wang, Chunzhi
Liu, Jinhang
Gao, Rong
Wei, Ming
Kochan, Orest
Powiązania:
https://bibliotekanauki.pl/articles/2087016.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
short-term electric load forecast
extreme learning machine
Lévy flight
tree-seed algorithm
Kernel principal component analysis
Opis:
In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 2; 153--162
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cost-sensitive feature selection
Selekcja cech z uwzględnieniem kosztu ich pozyskania
Autorzy:
Ciupke, K.
Powiązania:
https://bibliotekanauki.pl/articles/327830.pdf
Data publikacji:
2006
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
selekcja cech
algorytm mrówkowy
uczenie maszynowe
sztuczna inteligencja
diagnostyka techniczna
feature selection
ant algorithm
machine learning
artificial intelligence
technical diagnostics
Opis:
The paper concerns the selection of features in the technical diagnostics domain. The author focused his attention on a wrapper approach. In this approach an application of the ant algorithm as a search engine is proposed. The proposed method of so-called ant wrapper approach is presented. The method takes advantage of cost of features, where the cost is connected with the cost of sensors. The algorithm as a pseudo-code and some results of a verification experiment are shown. The verification was carried out on data derived from an active diagnostic experiment concerning a rotating machine. The obtained results show, that the proposed method could allow to reduce the number of used sensors.
W artykule opisano metodę selekcji cech z zastosowaniem algorytmu mrówkowego. Metoda pozwala także na uwzględnienie kosztu atrybutu, przy czym jego koszt związany jest z kosztem pozyskanie sygnału diagnostycznego. W przypadku gdy sygnał ten jest już wykorzystywany uznaje się, że koszt wyznaczenia danej cechy jest pomijalnie mały. Metodę przedstawiono w postaci pseudo-kodu i zweryfikowano dla danych pochodzących z czynnego eksperymentu diagnostycznego. Uzyskane wyniki pokazują, że istnieje możliwość ograniczenia liczby stosowanych czujników.
Źródło:
Diagnostyka; 2006, 2(38); 45-48
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zastosowanie algorytmów przeszukiwania grafów do analizy obrazów medycznych
Analysis of medical images based on graph search algorithms
Autorzy:
Dimitrova-Grekow, T.
Dąbkowski, A.
Powiązania:
https://bibliotekanauki.pl/articles/156629.pdf
Data publikacji:
2012
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
analiza obrazów medycznych
algorytmy przeszukiwania grafów
uczenie maszynowe
eksploracja danych
rozpoznawanie choroby
image analysis
graph search algorithm
machine learning
data mining
disease recognition
Opis:
W artykule przedstawiono wyniki testów niekonwencjonalnego zastosowania metod do przeszukiwania grafów w celu analizy obrazów powstałych z rezonansu magnetycznego głowy. Zaprezentowano GUI do automatycznej obróbki serii obrazów. Zbudowane klasyfikatory wykazały, że metoda BFS analizy plików DICOM, po odpowiednej selekcji cech, pozwala na 100% rozpoznawanie chorych na wodogłowie i ponad 90% zdrowych, co zachęca do dalszych badań i obserwacji, np. czy osoby sklasyfikowane błędnie jako chorzy, po czasie rzeczywiście nie rozwinęli tej choroby.
There are many methods for image segmentation [1, 2]: threshold, area, edge and hybrid methods. Area methods indicate groups of similar pixels form local regions [3, 4]. Edge methods detect boundaries between homogeneous segments [5, 6, 7]. In this paper we present the results of tests of unconventional implementation of graph search methods for the analysis of images generated from magnetic resonance imaging [8]. We explored the effectiveness of different approaches for dividing areas within a similar gray scale, using adapted graph search algorithms (DFS, BFS) after appropriate modification (Fig. 1). For this purpose, the Weka package (a tool for pre-processing, classification, regression, clustering and data visualization) was used [9]. A training set was generated after analyzing all the series of images from the database. First, we evaluated models created using certain algorithms and compared their efficacy (Tab. 1). This was followed by a selection of attributes (Tab. 2) and a re-evaluation of the models (Tab. 3). Comparison of the results of both evaluations showed that after selection of the relevant product attributes, you can achieve up to 100% detection of patients with hydrocephalus and over 90% proper recognition of healthy persons. This encourages further research and observation, such as whether persons wrongly classified as sick actually developed the disease in time. We designed a web application for the study, written in Windows Azure, as well as a GUI for automatic processing of a series of images (Fig. 2).
Źródło:
Pomiary Automatyka Kontrola; 2012, R. 58, nr 7, 7; 578-580
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
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:
Analiza wybranych metod tworzenia sztucznej inteligencji na przykładzie popularnej gry w karty
Analysis of selected methods of creating artificial intelligence on the example of a popular card game
Autorzy:
Gałka, Łukasz
Dzieńkowski, Mariusz
Powiązania:
https://bibliotekanauki.pl/articles/98306.pdf
Data publikacji:
2020
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
artificial intelligence
machine learning
algorithm efficiency evaluation
computer games
sztuczna inteligencja
uczenie maszynowe
ocena skuteczności algorytmów
gry komputerowe
Opis:
The aim of the article was to analyze selected methods of creating artificial intelligence in a popular card game. Two experiments were conducted: with a human and with a computer. The following algorithms were analyzed: random, min-max, based on a neural network, statistical and statistical with the use of “cheating” technique. The examined parameters were as follows: efficiency, execution time, number of implementation code lines, implementation time and training duration. The indicator with the greatest impact on the selection of the most optimal method was efficiency. The research has shown no difference in efficiency for the neural network-based algorithm and the statistical algorithm. In other cases, the differences in this feature were significant. The use of the “cheating” technique has increased the efficiency.
Celem artykułu była analiza wybranych metod tworzenia sztucznej inteligencji w popularnej grze w karty. Zostały przeprowadzone dwa eksperymenty: z człowiekiem oraz z komputerem. Analizie poddano algorytmy: losowy, minmax, bazujący na sieci neuronowej, statystyczny oraz statystyczny z użyciem techniki „oszukiwania”. Zbadano takie parametry jak: skuteczność, czas wykonania, liczbę linii kodu implementacji, czas implementacji oraz czas trwania treningu. Wskaźnikiem mającym największy wpływ na wybór najbardziej optymalnej metody była skuteczność. Badania wykazały brak różnic w skuteczności dla algorytmu bazującego na sieci neuronowej i algorytmu statystycznego. W pozostałych przypadkach różnice tej cechy były istotne. Użycie techniki „oszukiwania” zwiększyło skuteczność.
Źródło:
Journal of Computer Sciences Institute; 2020, 16; 233-240
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
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:
Man-algorithm Cooperation Intelligent Design of Clothing Products in Multi Links
Autorzy:
Han, Chen
Lei, Shen
Shaogeng, Zhang
Mingming, Wang
Ying, Tang
Powiązania:
https://bibliotekanauki.pl/articles/2056307.pdf
Data publikacji:
2022
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
intelligent algorithm
cooperation
clothing design
machine learning
efficiency
Opis:
The changes in technology have led to a synchronous change in the clothing design method, as well as media and artistic aesthetics in the same period. The intelligence algorithm is constantly increasing its participation in development and production in the clothing industry. In this study, a variety of intelligent algorithms, including the parameterised numer state algorithm, Generative Adversarial Networks, and style transfer were introduced into the multi-links of clothing product design and development, such as clothing shape, print pattern, texture craft, product vision, and so on. Then, an innovative clothing design method based on the cooperation of the intelligent algorithm and various human functional roles was constructed. The method improves the efficiency of the multiple links of clothing design, such as generating 10000 printing patterns every 72.12 seconds, and completing the migration of 92.7 frames of the garment process style every second. To a certain extent, this study realizes the scale economy of clothing design and reduces its marginal cost through the unlimited computing power brought about by Moore’s law of digital technology, which provides a reference for the exploration of clothing design in the era of industry 4.0.
Źródło:
Fibres & Textiles in Eastern Europe; 2022, 1 (151); 59--66
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A survey on prediction of diabetes using classification algorithms
Autorzy:
Khanwalkar, A.
Soni, R.
Powiązania:
https://bibliotekanauki.pl/articles/1818807.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
Tematy:
diabetes
diabetes prediction
algorithm
data mining
machine learning
cukrzyca
algorytm
eksploracja danych
uczenie maszynowe
Opis:
Purpose: Diabetes is a chronic disease that pays for a large proportion of the nation's healthcare expenses when people with diabetes want medical care continuously. Several complications will occur if the polymer disorder is not treated and unrecognizable. The prescribed condition leads to a diagnostic center and a doctor's intention. One of the real-world subjects essential is to find the first phase of the polytechnic. In this work, basically a survey that has been analyzed in several parameters within the poly-infected disorder diagnosis. It resembles the classification algorithms of data collection that plays an important role in the data collection method. Automation of polygenic disorder analysis, as well as another machine learning algorithm. Design/methodology/approach: This paper provides extensive surveys of different analogies which have been used for the analysis of medical data, For the purpose of early detection of polygenic disorder. This paper takes into consideration methods such as J48, CART, SVMs and KNN square, this paper also conducts a formal surveying of all the studies, and provides a conclusion at the end. Findings: This surveying has been analyzed on several parameters within the poly-infected disorder diagnosis. It resembles that the classification algorithms of data collection plays an important role in the data collection method in Automation of polygenic disorder analysis, as well as another machine learning algorithm. Practical implications: This paper will help future researchers in the field of Healthcare, specifically in the domain of diabetes, to understand differences between classification algorithms. Originality/value: This paper will help in comparing machine learning algorithms by going through results and selecting the appropriate approach based on requirements.
Źródło:
Journal of Achievements in Materials and Manufacturing Engineering; 2021, 104, 2; 77--84
1734-8412
Pojawia się w:
Journal of Achievements in Materials and Manufacturing Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Clothing Image Classification with a Dragonfly Algorithm Optimised Online Sequential Extreme Learning Machine
Klasyfikacja obrazu odzieży za pomocą zoptymalizowanego algorytmu Dragonfly sekwencyjnej maszyny uczącej się
Autorzy:
Li, Jianqiang
Shi, Weimin
Yang, Donghe
Powiązania:
https://bibliotekanauki.pl/articles/1419412.pdf
Data publikacji:
2021
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych
Tematy:
Dragonfly algorithm
Online Sequential Extreme Learning Machine
clothing image classification
optimised parameter
algorytm Dragonfly
OSELM
maszyna ucząca się
klasyfikacja obrazu odzieży
parametr zoptymalizowany
Opis:
This study proposes a solution for the issue of the low classification accuracy of clothing images. Using Fashion-MNIST as the clothing image dataset, we propose a clothing image classification technology based on an online sequential extreme learning machine (OSELM) optimised by the dragonfly algorithm (DA). First, we transform the Fashion-MNIST dataset into a data set that we extract from the corresponding grey image. Then, considering that the input weight and hidden layer bias of an OSELM are generated randomly, a DA is proposed to optimise the input weight and hidden layer bias of the OSELM to reduce the influence of random generation on the classification results. Finally, the optimised OSELM is applied to the clothing image classification. Compared to the other seven types of classification algorithms, the proposed clothing image classification model with the DA-optimised OSELM reached 93.98% accuracy when it contained 350 hidden nodes. Its performance was superior to other algorithms that were configured with the same number of hidden nodes. From a stability analysis of the box-plot, it was found that there were no outliers exhibited by the DA-OSELM model, whereas some other models had outliers or had lower stability compared to the model proposed, thereby validating the efficacy of the solution proposed.
W pracy zaproponowano rozwiązanie problemu niskiej dokładności klasyfikacyjnej obrazów odzieży. Wykorzystując Fashion-MNIST jako zbiór danych obrazu odzieży, zaproponowano technologię klasyfikacji obrazów odzieży w oparciu o sekwencyjną maszynę uczącą się (OSELM) zoptymalizowaną przez algorytm Dragonfly (DA). Najpierw przekształcono zbiór danych Fashion-MNIST w zestaw danych, który wyodrębniono z obrazu. Następnie, biorąc pod uwagę, że waga wejściowa i odchylenie warstwy ukrytej OSELM były generowane losowo, w celu zmniejszenia wpływu generowania losowego na wyniki klasyfikacji zaproponowano DA w celu optymalizacji wagi wejściowej i obciążenia warstwy ukrytej OSELM. Następnie, zoptymalizowany OSELM zastosowano do klasyfikacji obrazu odzieży. W porównaniu z pozostałymi siedmioma typami algorytmów klasyfikacji, proponowany model klasyfikacji obrazu odzieży ze zoptymalizowanym przez DA OSELM osiągnął dokładność 93,98%. Jego wydajność przewyższyła inne algorytmy. Na podstawie analizy stabilności wykresu stwierdzono, że nie było wartości odstających wykazywanych przez model DA-OSELM, podczas gdy niektóre inne modele miały wartości odstające lub miały niższą stabilność w porównaniu z proponowanym modelem, potwierdzono w ten sposób skuteczność proponowanego rozwiązania.
Źródło:
Fibres & Textiles in Eastern Europe; 2021, 3 (147); 91-96
1230-3666
2300-7354
Pojawia się w:
Fibres & Textiles in Eastern Europe
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 25

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