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Tytuł:
Predicting banking stock prices using RNN, LSTM, and GRU approach
Autorzy:
Satria, Dias
Tematy:
GRU
Indonesia Stock Price Prediction
machine learning
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Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Powiązania:
https://bibliotekanauki.pl/articles/30148273.pdf  Link otwiera się w nowym oknie
Opis:
In recent years, the implementation of machine learning applications started to apply in other possible fields, such as economics, especially investment. But, many methods and modeling are used without knowing the most suitable one for predicting particular data. This study aims to find the most suitable model for predicting stock prices using statistical learning with Arima Box-Jenkins, RNN, LSTM, and GRU deep learning methods using stock price data for 4 (four) major banks in Indonesia, namely BRI, BNI, BCA, and Mandiri, from 2013 to 2022. The result showed that the ARIMA Box-Jenkins modeling is unsuitable for predicting BRI, BNI, BCA, and Bank Mandiri stock prices. In comparison, GRU presented the best performance in the case of predicting the stock prices of BRI, BNI, BCA, and Bank Mandiri. The limitation of this research was data type was only time series data. It limits our instrument to four statistical methode only.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multimodal Sarcasm Detection via Hybrid Classifier with Optimistic Logic
Autorzy:
Bavkar, Dnyaneshwar Madhukar
Kashyap, Ramgopal
Khairnar, Vaishali
Tematy:
Bi-GRU
improved CCA
LSTM
multimodal sarcasm detection
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Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:
https://bibliotekanauki.pl/articles/2142320.pdf  Link otwiera się w nowym oknie
Opis:
This work aims to provide a novel multimodal sarcasm detection model that includes four stages: pre-processing, feature extraction, feature level fusion, and classification. The pre-processing uses multimodal data that includes text, video, and audio. Here, text is pre-processed using tokenization and stemming, video is pre-processed during the face detection phase, and audio is pre-processed using the filtering technique. During the feature extraction stage, such text features as TF-IDF, improved bag of visual words, n-gram, and emojis as well on the video features using improved SLBT, and constraint local model (CLM) are extraction. Similarly the audio features like MFCC, chroma, spectral features, and jitter are extracted. Then, the extracted features are transferred to the feature level fusion stage, wherein an improved multilevel canonical correlation analysis (CCA) fusion technique is performed. The classification is performer using a hybrid classifier (HC), e.g. bidirectional gated recurrent unit (Bi-GRU) and LSTM. The outcomes of Bi-GRU and LSTM are averaged to obtain an effective output. To make the detection results more accurate, the weight of LSTM will be optimally tuned by the proposed opposition learning-based aquila optimization (OLAO) model. The MUStARD dataset is a multimodal video corpus used for automated sarcasm Discovery studies. Finally, the effectiveness of the proposed approach is proved based on various metrics.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic method of macular diseases detection using deep cnn-gru network in oct images
Autorzy:
Powroznik, Paweł
Skublewska-Paszkowska, Maria
Rejdak, Robert
Nowomiejska, Katarzyna
Tematy:
Drusen
Deep CNN-GRU
AMD classification
OCT
deep learning
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Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Powiązania:
https://bibliotekanauki.pl/articles/58907191.pdf  Link otwiera się w nowym oknie
Opis:
The increasing development of Deep Learning mechanism allowed ones to create semi-fully or fully automated diagnosis software solutions for medical imaging diagnosis. The convolutional neural networks are widely applied for central retinal diseases classifi-cation based on OCT images. The main aim of this study is to propose a new network, Deep CNN-GRU for classification of early-stage and end-stages macular diseases as age-related macular degeneration and diabetic macular edema (DME). Three types of disorders have been taken into consideration: drusen, choroidal neovascularization (CNV), DME, alongside with normal cases. The created automatic tool was verified on the well-known Labelled Optical Coherence Tomography (OCT) dataset. For the classifier evaluation the following measures were calculated: accuracy, precision, recall, and F1 score. Based on these values, it can be stated that the use of a GRU layer directly connected to a convolutional network plays a pivotal role in improving previously achieved results. Additionally, the proposed tool was compared with the state-of-the-art of deep learning studies performed on the Labelled OCT dataset. The Deep CNN-GRU network achieved high performance, reaching up to 98.90% accuracy. The obtained results of classification performance place the tool as one of the top solutions for diagnosing retinal diseases, both early and late stage.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Zarys funkcjonowania legalnych rezydentur 1. Zarządu Głównego KGB w Japonii oraz współczesne reminiscencje tego zjawiska
Outline of the functioning of the legal residences of the 1st Chief Directorate of the KGB in Japan and contemporary reminiscences of this phenomenon
Autorzy:
Pawlikowicz, Leszek
Tematy:
intelligence
residency
KGB
GRU
SWR
Japan
rezydentura
Japonia
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Wydawca:
Uniwersytet Rzeszowski. Wydawnictwo Uniwersytetu Rzeszowskiego
Powiązania:
https://bibliotekanauki.pl/articles/59555558.pdf  Link otwiera się w nowym oknie
Opis:
W  artykule – na podstawie japońskich i amerykańskich dokumentów rządowych i przy wykorzystaniu metod empirycznych, ilościowych i porównawczych – poddano analizie działalność prowadzoną KGB w Japonii przy wykorzystaniu tamtejszych legalnych rezydentur  w latach 1954-1991. Poza przedstawieniem ich liczby i lokalizacji, a także ewolucji liczby pracowników kadrowych KGB oraz określeniem ich proporcji do analogicznych pracowników GRU, jak również do wszystkich pracowników różnego rodzaju oficjalnych przedstawicielstw ZSRR, zaprezentowano ponadto zasadnicze kierunki działań komitetu w kraju “kwitnącej wiśni” oraz skalę penetracji japońskich instytucji poprzez osobowe źródła informacji. Dodatkowo próbowano porównać wspomniane dane ,odnoszące się do okresu zimnej wojny do analogicznych – choć znacznie bardziej fragmentarycznych i szczątkowych – dotyczących współczesności.
The article analyses, on the basis of Japanese and American governmental documents and using empirical, quantitative and comparative methods, the activities carried out by the KGB in Japan with the use of its legal residences in the years 1954-1991. Apart from presenting their number and location, as well as the evolution of the number of KGB personnel and defining their proportion to analogous GRU employees, as well as to all employees of various official USSR representations, it also presents the basic directions of the committee's activities in the ‘blooming cherry country’ and the scale of penetration of Japanese institutions through personal sources of information. In addition, an attempt has been made to compare the aforementioned data relating to the Cold War period with analogous - albeit much more fragmentary and residual - data relating to the present day.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of convolutional gated recurrent units u-net for distinguishing between retinitis pigmentosa and cone–rod dystrophy
Autorzy:
Skublewska-Paszkowska, Maria
Powroznik, Paweł
Rejdak, Robert
Nowomiejska, Katarzyna
Tematy:
retinitis pigmentosa
convolutional GRU U-Net
classification
UWFP
UWFAF
deep learning
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Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Powiązania:
https://bibliotekanauki.pl/articles/58907234.pdf  Link otwiera się w nowym oknie
Opis:
Artificial Intelligence (AI) has gained a prominent role in the medical industry. The rapid development of the computer science field has caused AI to become a meaningful part of modern healthcare. Image-based analysis involving neural networks is a very important part of eye diagnoses. In this study, a new approach using Convolutional Gated Recurrent Units (GRU) U-Net was proposed for the classifying healthy cases and cases with retinitis pigmentosa (RP) and cone–rod dystrophy (CORD). The basis for the classification was the location of pigmentary changes within the retina and fundus autofluorescence (FAF) pattern, as the posterior pole or the periphery of the retina may be affected. The dataset, gathered in the Chair and Department of General and Pediatric Ophthalmology of Medical University in Lublin, consisted of 230 ultra-widefield pseudocolour (UWFP) and ultra-widefield FAF images, obtained using the Optos 200TX device (Optos PLC). The data were divided into three categories: healthy subjects (50 images), patients with CORD (48 images) and patients with RP (132 images). For applying deep learning classification, which rely on a large amount of data, the dataset was artificially enlarged using augmentation involving image manipulations. The final dataset contained 744 images. The proposed Convolutional GRU U-Net network was evaluated taking account of the following measures: accuracy, precision, sensitivity, specificity and F1. The proposed tool achieved high accuracy in a range of 91.00%–97.90%. The developed solution has a great potential in RP diagnoses as a supporting tool.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of Recurrent Neural Networks for User Verification based on Keystroke Dynamics
Autorzy:
Kobojek, P.
Saeed, K.
Tematy:
biometrics
GRU networks
keystroke dynamics
LSTM networks
recurrent neural networks
user verification
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Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:
https://bibliotekanauki.pl/articles/307650.pdf  Link otwiera się w nowym oknie
Opis:
Keystroke dynamics is one of the biometrics techniques that can be used for the verification of a human being. This work briefly introduces the history of biometrics and the state of the art in keystroke dynamics. Moreover, it presents an algorithm for human verification based on these data. In order to achieve that, authors’ training and test sets were prepared and a reference dataset was used. The described algorithm is a classifier based on recurrent neural networks (LSTMand GRU). High accuracy without false positive errors as well as high scalability in terms of user count were chosen as goals. Some attempts were made to mitigate natural problems of the algorithm (e.g. generating artificial data). Experiments were performed with different network architectures. Authors assumed that keystroke dynamics data have sequence nature, which influenced their choice of classifier. They have achieved satisfying results, especially when it comes to false positive free setting.
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-10 z 50

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