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EN
Purpose: The aim of the research is the evaluation of the ecological state of development based on statistical data from voivodeships in Poland. Design/methodology/approach: The research uses selected methods of multivariate comparative analysis, in particular, linear ordering. The analysis of the differentiation of the level of ecological development by voivodeships in Poland made it possible to order the provinces according to the indicators that represent the state of the environmental situation. After the process of ordering, the process of grouping voivodeships was possible. The relevant calculations were made using QGIS and Statistica software. Findings: The result of the analysis presents a tree main cluster with similar voivodeships according to ecological situation. Practical implications: The presented methods enable continuous monitoring and control of progress in the implementation of the assumed ecological goals. Green development assessment methods can also help monitor progress towards the Sustainable Development Goals over time. This can help identify trends and patterns and provide feedback on the effectiveness of policies and programs. The results of the analyses may be a useful tool for monitoring and evaluating Poland's progress in achieving the assumed ecological goals of the European Union by 2030. Originality/value: These studies are a very useful tool in identifying the ecological situation and directing administrative activities to the appropriate regions in the country.
EN
Online saree shopping has become a popular way for adolescents to shop for fashion. Purchasing from e-commerce is a huge time-saver in this situation. Female apparel has many difficult-to-describe qualities, such as texture, form, colour, print, and length. Research involving online shopping often involves studying consumer behaviour and preferences. Fashion image analysis for product search still faces difficulties in detecting textures based on query images. To solve the above problem, a novel deep learning-based SareeNet is presented to quickly classify the tactile sensation of a saree according to the user’s query. The proposed work consists of three phases: i) saree image pre-processing phase, ii) patch generation phase, and iii) texture detection and optimization for efficient classification. The input image is first denoised using a contrast stretching adaptive bilateral (CSAB) filter. The deep learning-based mask region-based convolutional neural network (Mask R-CNN) divides the region of interest into saree patches. A deep learning-based improved EfficientNet-B3 has been introduced which includes an optimized squeeze and excitation block to categorise 25 textures of saree images. The Aquila optimizer is applied within the squeeze and excitation block of the improved EfficientNet to normalise the parameters for improving the accuracy in saree texture classification. The experimental results show that SareeNet is effective in categorising texture in saree images with 98.1% accuracy. From the experimental results, the proposed improved EfficientNet-B3 improves overall accuracy by 2.54%, 0.17%, 2.06%, 1.78%, and 0.63%, for MobileNet, DenseNet201, ResNet152, and InspectionV3, respectively.
EN
The prevalence of lifestyle diseases and trends related to healthy eating contribute to the constant search for chemical compounds with specific biological activity. Studies are conducted on plants and substances of natural origin that have been used in medicine for millennia. Techniques of vibrational spectroscopy are an underrated group of methods enabling direct analysis of plant raw material and food in their native forms. The presented examples of Arabidopsis tissues, various species and hybrids of poplar and Cistus herb classification, as well as quantitative analyses of active compounds in plant material and pharmaceutical products and determination of physicochemical parameters of common food (i.e. milk, yoghurts, pasta and flour), demonstrate the possibility of using vibrational spectroscopy for comprehensive analysis of samples of natural origin. Typical measurement techniques and chemometric methods are briefly described in this paper. The scheme of quantitative analysis based on vibrational spectra is shown and the impact of selected experimental parameters on the accuracy of the obtained results is discussed. The imaging techniques used to analyse the changes in plant tissue structures caused by genetic mutations were also presented.
EN
The main aim of this paper is to evaluate crawlers collecting the job offers from websites. In particular the research is focused on checking the effectiveness of ensemble machine learning methods for the validity of extracted position from the job ads. Moreover, in order to significantly reduce the training time of the algorithms (Random Forests and XGBoost), granularity methods were also tested to significantly reduce the input training dataset. Both methods achieved satisfactory results in accuracy and F1 measures, which exceeded 96%. In addition, granulation reduced the input dataset by more than 99%, and the results obtained were only slightly worse (accuracy between 1% and 5%, F1 between 3% and 8%). Thus, it can be concluded that the considered methods can be used in the evaluation of job web crawlers.
EN
In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects.
PL
W badaniach naukowych obserwuje się coraz większe zainteresowanie wykorzystaniem sztucznej inteligencji do poszukiwania rozwiązań trudnych problemów naukowych. W niniejszym artykule został zastosowany algorytm głębokiego uczenia z użyciem obrazów próbek materiałów wykorzystywanych do budowy nawierzchni drogowych. Fotografie przedstawiały przekroje losowych próbek wykonane za pomocą tomografu komputerowego. Do analizy wykorzystano próbki historyczne, znajdujące się w bazie danych zbierającej informacje z wielu lat. Analizę głębokiego uczenia wykonano przy użyciu niektórych elementów architektury sieci VGG16 i zaimplementowano, stosując język R. Dane uczące oraz treningowe poddano augmentacji oraz walidacji krzyżowej. W rezultacie uzyskano wysoki poziom 96,4% jakości identyfikacji rodzaju próbki oraz jej wybranych cech strukturalnych. Fotografie w zbiorze identyfikacyjnym zostały poprawnie zidentyfikowane pod względem struktury, typu mieszanki oraz uziarnienia. Wytrenowany model w bardzo dobry sposób zidentyfikował próbki w obszarze dziedziny trenowanego zbioru danych. W rezultacie taka metodyka może w przyszłości ułatwić identyfikację rodzaju mieszanki, jej podstawowych właściwości oraz defektów.
EN
Predicting epileptic seizures in advance improves greatly the life of epileptic patients. In this paper we present a new approach based on patient specific channel optimization using four different features namely entropy, variance, kurtosis and skewness. After selecting three best channels for each method, we then use Convolutional Neural Network (CNN) to classify raw EEG signal in order to discriminate between interictal and preictal state. With entropy, our method achieves a good degree of prediction in terms of accuracy 97.09%, sensitivity 97.67% and specificity 96.51% for patient 01 using channels 4, 8 and 20.
PL
Przewidywanie napadów padaczkowych z wyprzedzeniem znacznie poprawia życie chorych na padaczkę. W tym artykule prezentujemy nowe podejście oparte na optymalizacji kanałów specyficznych dla pacjenta przy użyciu czterech różnych metod, a mianowicie entropii, wariancji, kurtozy i skośności. Po wybraniu trzech najlepszych kanałów dla każdej z metod, wykorzystujemy Neuronową Sieć Konwolucyjną (CNN) do klasyfikacji surowego sygnału EEG w celu rozróżnienia pomiędzy stanem międzynapadowym i przednapadowym. Dzięki entropii nasza metoda osiąga dobry stopień predykcji w zakresie dokładności 97,09%, czułości 97,67% i specyficzności 96,51% dla pacjenta 01 przy użyciu kanałów 4, 8 i 20.
7
Content available remote Machine learning to diagnose breast cancer
EN
As the number of breast cancer diseases is increasing rapidly every year, new technologies are utilized to predict and diagnose this disease for better women's lives worldwide. The development of Machine Learning can be utilized to contribute in this sense and help in the early diagnosis of breast cancer. This paper aims to predict and diagnose breast cancer using Machine Learning techniques such as support vector Machine (SVM) and Decision -tree and Nearest neighbour (KNN). The results show the out performance of SVM over the other methods. These methods can be very helpful to predict the breast cancer disease ahead of time.
PL
Ponieważ liczba zachorowań na raka piersi gwałtownie rośnie z roku na rok, nowe technologie są wykorzystywane do przewidywania i diagnozowania tej choroby w celu poprawy życia kobiet na całym świecie. Rozwój uczenia maszynowego może być wykorzystany do wniesienia wkładu w tym sensie i pomocy we wczesnej diagnozie raka piersi. Niniejszy artykuł ma na celu przewidywanie i diagnozowanie raka piersi przy użyciu technik uczenia maszynowego, takich jak maszyna wektora nośnego (SVM) oraz drzewo decyzyjne i najbliższy sąsiad (KNN). Wyniki pokazują wydajność SVM w porównaniu z innymi metodami. Metody te mogą być bardzo pomocne w przewidywaniu zgonów na raka piersi z wyprzedzeniem.
EN
Women are particularly vulnerable to breast cancer. Breast cancer diagnosis has benefited greatly from the utilization of ultrasound imaging. Breast UltraSound (BUS) image segmentation remains a difficult challenge due to low image quality. Furthermore, BUS image segmentation, as well as classification, is an important stage in the analysis process. Initially, the image associated with breast cancer is gathered from MIAS database. The gathered image undergoes pre-processing operation using the adaptive median filtering technique. Subsequently, the segmentation is performed in the pre-processed images using the hybrid method consisting of GMM and K-Means. These segmented images undergo the feature extraction steps further where the features are extracted by utilizing the Gray Level Co-occurrence Matrix (GLCM). Grey Wolf Optimization (GWO) selects the optimal features for further classification using a novel 1D Convolution LSTM. Here, the pooling layer of 1D CNN is replaced by the LSTM. The objective function behind the optimal feature selection and classification is the accuracy maximization. Finally, the novel One Dimensional Convolution Long Short Term Memory (1 DCLSTM) classifies the outcome into normal, benign, and malignant, respectively. The proposed method is compared with the other state of art methods related to this research.
PL
Kobiety są szczególnie narażone na raka piersi. Diagnostyka raka piersi bardzo skorzystała na wykorzystaniu obrazowania ultrasonograficznego. Segmentacja obrazu UltraSound (BUS) piersi pozostaje trudnym wyzwaniem ze względu na niską jakość obrazu. Ponadto segmentacja obrazu BUS, a także klasyfikacja, jest ważnym etapem procesu analizy. Początkowo obraz związany z rakiem piersi pozyskiwany jest z bazy MIAS. Zgromadzony obraz jest poddawany wstępnemu przetwarzaniu przy użyciu techniki adaptacyjnego filtrowania medianowego. Następnie na wstępnie przetworzonych obrazach przeprowadzana jest segmentacja metodą hybrydową składającą się z GMM i K-Means. Te podzielone na segmenty obrazy przechodzą kolejne etapy ekstrakcji cech, w których cechy są wyodrębniane przy użyciu macierzy współwystępowania poziomu szarości (GLCM). Optymalizacja Gray Wolf (GWO) wybiera optymalne funkcje do dalszej klasyfikacji przy użyciu nowatorskiego rozwiązania 1D Convolution LSTM. W tym przypadku warstwa łączenia 1D CNN zostaje zastąpiona przez LSTM. Funkcją celu stojącą za optymalnym doborem i klasyfikacją cech jest maksymalizacja dokładności. Wreszcie, powieść jednowymiarowa pamięć krótkoterminowa z konwolucją jednowymiarową (1 DCLSTM) klasyfikuje wynik odpowiednio na normalny, łagodny i złośliwy. Proponowana metoda jest porównywana z innymi nowoczesnymi metodami związanymi z tymi badaniami.
9
Content available Many Faces of Singularities in Robotics
EN
In this survey paper some issues concerning a singularity concept in robotics are addressed. Singularities are analyzed in the scope of inverse kinematics for serial manipulator, a motion planning task of nonholonomic systems and the optimal control covering a large area of practical robotic systems. An attempt has been made to define the term singularity, which is independent on a specific task. A few classifications of singularities with respect to different criteria are proposed and illustrated on simple examples. Singularities are analyzed from a numerical and physical point of view. Generally, singularities pose some problems in motion planning and/or control of robots. However, as illustrated on the example on force/momenta transformation in serial manipulators, they can also be desirable is some cases. Singularity detection techniques and some methods to cope with them are also provided. The paper is intended to be didactic and to help robotic researchers to get a general view on the singularity issue.
PL
W przeglądowym artykule przedstawiono wybrane zagadnienia dotyczące różnych koncepcji osobliwości spotykanych w robotyce. Analizowane są osobliwości w zadaniu odwrotnej kinematyki dla manipulatorów szeregowych, planowaniu ruchu układów nieholonomicznych oraz sterowaniu optymalnym. Rozważane zadania obejmują duży obszar praktycznych systemów robotycznych. Podjęto próbę zdefiniowania pojęcia osobliwości niezależne od konkretnego zadania. Zaproponowano kilka klasyfikacji osobliwości w zależności do różnych kryteriów oraz zilustrowanych na prostych przykładach. Osobliwości przeanalizowano z numerycznego i fizycznego punktu widzenia. Ogólnie, osobliwości stwarzają pewne problemy w planowaniu ruchu i/lub sterowaniu robotami. Jednakże, jak pokazano na przykładzie transformacji sił/momentów w manipulatorach szeregowych, w niektórych przypadkach mogą one być również użyteczne. Przedstawiono także techniki wykrywania osobliwości oraz metody radzenia sobie z nimi. Praca w założeniu ma charakter dydaktyczny i ma pomóc badaczom z kręgu robotyki uzyskać ogólny pogląd na zagadnienie osobliwości.
EN
The classification and separation of minerals happen in the traditional gravity separation simultaneously. This paper focuses on the classification performance of quartz particles in the enhanced gravity field. The classification efficiency of single quartz particles decreased then increased with the increase of rotational angular velocity, while it decreased with the increase of backwash water pressure. The classification efficiency of -0.5 +0.25mm, -0.25 +0.125mm, -0.125 +0.074mm, -0.074 +0.045mm and -0.045mm quartz was higher than the corresponding narrow size of -0.5mm quartz in general. The “fish-hook” phenomenon appeared in the partition curve of -0.5mm quartz under small/large rotational angular velocity and small backwash water pressure, and the dip point could be found in fine particles region, which indicated that the “fish-hook” was closely related with operating parameters and particle size. A medium rotational angular velocity and larger backwash water pressure could be helpful to avoid the appearance of “fish-hook” in fine particles region and achieve a better classification performance. This investigation is beneficial to understand the regularity of particle migration in the enhanced gravity field.
EN
The Cyclonic Continuous Centrifugal Separator (CCCS) is a new type of separation equipment developed based on cyclonic continuous centrifugal separation technology and combined with the separation principle of the fluidized bed. Taking hematite as the research object, the main parameters and conditions of the best hematite classification were determined through the classification test by using CCCS. Based on the classification test, the significance order of each process parameter and their interaction with hematite classification efficiency of the underflow products was analyzed with the Response Surface Methodology, the optimal process parameter of hematite classification was obtained and a multiple regression equation was established. The optimized process conditions were as follows, feeding pressure 55.48 kPa, backwash pressure 9.79 kPa, and underflow pressure 31.94 kPa. Under these conditions, the average hematite ore classification efficiency of coarse fraction (-2~+0.15mm), medium fraction (-0.15~+0.074mm) and fine fraction (-0.074mm) were 85.08%, 65.10% and 51.41%, respectively, and the relative errors with the predicted values were 1.6%, 4.0% and 2.5%, respectively. The results showed that the analytical model has good predictive performance. This research provides a certain prospect for the application of Cyclonic Continuous Centrifugal Separation to hematite ore classification. it provides a reference for the application of the Response Surface Methodology in the classification of hematite by Cyclonic Continuous Centrifugal Separation.
EN
To address the problem that a deep neural network needs a sufficient number of training samples to have a good prediction performance, this paper firstly used the Z-Map algorithm to generate a simulated profile of the milling surface and construct an optical simulation model of surface imaging to supplement the training sample size of the neural network. Then the Deep CORAL model was used to match the textures of the simulated samples and the actual samples across domains to solve the problem that the simulated samples were not in the same domain as the actual milling samples. Experimental results have shown that high texture matching could be achieved between optical simulation images and actual images, laying the foundation for expanding the actual milled workpiece images with the simulation images. The deep convolutional neural model Xception was used to predict the classification of six classes of data sets with the inclusion of simulation images, and the accuracy was improved from 86.48% to 92.79% compared with the model without the inclusion of simulation images. The proposed method solves the problem of the need for a large number of samples for deep neural networks and lays the foundation for similar methods to predict surface roughness for different machining processes.
13
Content available remote Klasyfikacja metalowych pokryć samonośnych
EN
For many years, the amount of waste generated on a global scale has shown an increasing tendency and their management and logistic is becoming a growing problem for most countries in the world. Waste management is an important issue to be addressed, as it concerns the three basic pillars of sustainable development: social, economic, and environmental. Therefore, it seems necessary to take initiatives to reduce the amount of waste generated and improve the waste management system. The article aims to analyse changes in the way of waste management and logistics in the European Union countries and the classification of these countries on the basis of the achieved effects in waste management. The article analyses three selected factors that reflect the effects of achieving environmental objectives in waste management. The cluster analysis method was used for the analysis. It found that EU countries differ in the quality of the results achieved in waste management, depending on the achievement of environmental management and sustainability objectives. In addition, the results of the analysis showed that the time factor has a significant impact on the classification of countries. High dynamics of the quality of effects in waste management were observed in the period under review.
15
Content available Training CNN classifiers solely on webly data
EN
Real life applications of deep learning (DL) are often limited by the lack of expert labeled data required to effectively train DL models. Creation of such data usually requires substantial amount of time for manual categorization, which is costly and is considered to be one of the major impediments in development of DL methods in many areas. This work proposes a classification approach which completely removes the need for costly expert labeled data and utilizes noisy web data created by the users who are not subject matter experts. The experiments are performed with two well-known Convolutional Neural Network (CNN) architectures: VGG16 and ResNet50 trained on three randomly collected Instagram-based sets of images from three distinct domains: metropolitan cities, popular food and common objects - the last two sets were compiled by the authors and made freely available to the research community. The dataset containing common objects is a webly counterpart of PascalVOC2007 set. It is demonstrated that despite significant amount of label noise in the training data, application of proposed approach paired with standard training CNN protocol leads to high classification accuracy on representative data in all three above-mentioned domains. Additionally, two straightforward procedures of automatic cleaning of the data, before its use in the training process, are proposed. Apparently, data cleaning does not lead to improvement of results which suggests that the presence of noise in webly data is actually helpful in learning meaningful and robust class representations. Manual inspection of a subset of web-based test data shows that labels assigned to many images are ambiguous even for humans. It is our conclusion that for the datasets and CNN architectures used in this paper, in case of training with webly data, a major factor contributing to the final classification accuracy is representativeness of test data rather than application of data cleaning procedures.
EN
This study proposes a method that combines Histogram of Oriented Gradients (HOG) feature extraction and Extreme Gradient Boosting (XGBoost) classification to resolve the challenges of concrete crack monitoring. The purpose of the study is to address the common issue of overfitting in machine learning models. The research uses a dataset of 40,000 images of concrete cracks and HOG feature extraction to identify relevant patterns. Classification is performed using the ensemble method XGBoost, with a focus on optimizing its hyperparameters. This study evaluates the efficacy of XGBoost in comparison to other ensemble methods, such as Random Forest and AdaBoost. XGBoost outperforms the other algorithms in terms of accuracy, precision, recall, and F1-score, as demonstrated by the results. The proposed method obtains an accuracy of 96.95% with optimized hyperparameters, a recall of 96.10%, a precision of 97.90%, and an F1-score of 97%. By optimizing the number of trees hyperparameter, 1200 trees yield the greatest performance. The results demonstrate the efficacy of HOG-based feature extraction and XGBoost for accurate and dependable classification of concrete fractures, overcoming the overfitting issues that are typically encountered in such tasks.
EN
Knowledge about future traffic in backbone optical networks may greatly improve a range of tasks that Communications Service Providers (CSPs) have to face. This work proposes a procedure for long-term traffic forecasting in optical networks. We formulate a long-terT traffic forecasting problem as an ordinal classification task. Due to the optical networks’ (and other network technologies’) characteristics, traffic forecasting has been realized by predicting future traffic levels rather than the exact traffic volume. We examine different machine learning (ML) algorithms and compare them with time series algorithms methods. To evaluate the developed ML models, we use a quality metric, which considers the network resource usage. Datasets used during research are based on real traffic patterns presented by Internet Exchange Point in Seattle. Our study shows that ML algorithms employed for long-term traffic forecasting problem obtain high values of quality metrics. Additionally, the final choice of the ML algorithm for the forecasting task should depend on CSPs expectations.
EN
Following article address the issue of automatic knee disorder diagnose with usage of neural networks. We proposed several hybrid neuralnet architectures which aim to successfully classify abnormalityusing MRI (magnetic resonance imaging) images acquired from publicly available dataset. To construct such combinations of modelswe used pretrainedAlexnet, Resnet18 and Resnet34 downloaded from Torchvision. Experiments showedthat for certain abnormalities our models can achieve up to 90% accuracy.
PL
Niniejszy artykuł porusza temat automatycznej diagnozy uszkodzenia stawu kolanowego z zastosowaniem sieci neuronowych. Zaproponowanokilka hybrydowych sieci neuronowych, które podjęły próbę poprawnej klasyfikacji nieprawidłowości wykorzystując zdjęcia rezonansu magnetycznego pochodzące z publicznie dostępnego zbioru. Do konstrukcjikombinacji sieci skorzystanoz pretrenowanych modeli (Alexnet, Resnet18, Resnet34) pobranychz Torchvision. Eksperyment pokazał, że dla klasyfikacji niektórych schorzeń modele osiągnęły nawet 90% skuteczności.
EN
Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents.This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam,and Gandhinagar in Vijayawada(India)from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniquesare applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These modelsare trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.
PL
Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8946 ofiarami śmiertelnymiw 22311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiari ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkachz regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowychna śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tymwielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh.
EN
Opinions related to rising fuel prices need to be seen and analysed. Public opinion is closely related to public policy in Indonesia in the future. Twitter is one of the media that people use to convey their opinions. This study uses sentiment analysis to look at this phenomenon. Sentiment is divided into three categories: positive, neutral, and negative. The methods used in this research are Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours, and Adaptive Synthetic Random Forest. The Adaptive Synthetic method is used to handle unbalanced data. The data used in this study are public arguments per province in Indonesia. The results obtained in this study are negative sentiments that dominate all provinces in Indonesia. There is a relationship between negative sentiment and the level of education, internet use, and the human development index. Adaptive Synthetic Multinomial Naive Bayes performed better than other methods, with an accuracy of 0.882. The highest accuracy of the Adaptive Synthetic Multinomial Naive Bayes method is 0.990 in Papua Barat Province.
PL
Należy przyjrzeć się i przeanalizować opinie związane z rosnącymi cenami paliw. Opinia publiczna jest ściśle związana z polityką publiczną Indonezji w przyszłości. Twitter jest jednym z mediów, których ludzie używają do przekazywania swoich opinii. Niniejsze badanie wykorzystuje analizę nastrojów, aby przyjrzeć się temu zjawisku. Opinia jest podzielona na trzy kategorie: pozytywną, neutralną i negatywną. Metody wykorzystane w tym badaniu to Adaptive Synthetic Multinomial Naive Bayes, Adaptive Synthetic k-nearest neighbours i Adaptive Synthetic Random Forest. Metoda Adaptive Synthetic służy do obsługi niezrównoważonych danych. Dane wykorzystane w tym badaniu to argumenty publiczne według prowincji w Indonezji. Wyniki uzyskane w tym badaniu to negatywne nastroje, które dominują we wszystkich prowincjach Indonezji. Istnieje związek między negatywnymi nastrojami a poziomem wykształcenia, korzystaniem z Internetu i wskaźnikiem rozwoju społecznego. Adaptive Synthetic Multinomial Naive Bayes działała lepiej niż inne metody, z dokładnością 0,882. Najwyższa dokładność metody Adaptive Synthetic Multinomial Naive Bayes wynosi 0,990 w prowincji Papua Barat.
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