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Tytuł:
Position Estimation in Mixed Indoor-Outdoor Environment Using Signals of Opportunity and Deep Learning Approach
Autorzy:
Urwan, Sebastian
Wysocka, Dominika R.
Pietrzak, Alicja
Cwalina, Krzysztof K.
Powiązania:
https://bibliotekanauki.pl/articles/2134050.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
radiolocalization
deep neural network
hybrid localization
Opis:
To improve the user’s localization estimation in indoor and outdoor environment a novel radiolocalization system using deep learning dedicated to work both in indoor and outdoor environment is proposed. It is based on the radio signatures using radio signals of opportunity from LTE an WiFi networks. The measurements of channel state estimators from LTE network and from WiFi network are taken by using the developed application. The user’s position is calculated with a trained neural network system’s models. Additionally the influence of various number of measurements from LTE and WiFi networks in the input vector on the positioning accuracy was examined. From the results it can be seen that using hybrid deep learning algorithm with a radio signatures method can result in localization error 24.3 m and 1.9 m lower comparing respectively to the GPS system and standalone deep learning algorithm with a radio signatures method in indoor environment. What is more, the combination of LTE and WiFi signals measurement in an input vector results in better indoor and outdoor as well as floor classification accuracy and less positioning error comparing to the input vector consisting measurements from only LTE network or from only WiFi network.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 594-607
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Theory I: Deep networks and the curse of dimensionality
Autorzy:
Poggio, T.
Liao, Q.
Powiązania:
https://bibliotekanauki.pl/articles/200623.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep network
shallow network
convolutional neural network
function approximation
deep learning
sieci neuronowe
aproksymacja funkcji
uczenie głębokie
Opis:
We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning classification and recognition method for milling surface roughness combined with simulation data
Autorzy:
Lu, Lingli
Yi, Huaian
Shu, Aihua
Qin, Jianhua
Lu, Enhui
Powiązania:
https://bibliotekanauki.pl/articles/2203367.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
milling surface
classification
deep neural network
simulation
Opis:
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.
Źródło:
Metrology and Measurement Systems; 2023, 30, 1; 117--138
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting the equity premium: Do deep neural network models work?
Autorzy:
Zhou, Xianzheng
Zhou, Hui
Long, Huaigang
Powiązania:
https://bibliotekanauki.pl/articles/23943440.pdf
Data publikacji:
2023
Wydawca:
Fundacja Naukowa Instytut Współczesnych Finansów
Tematy:
equity premium
return predictability
deep neural network
asset allocation
forecasting performance
Opis:
This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.
Źródło:
Modern Finance; 2023, 1, 1; 1-11
2956-7742
Pojawia się w:
Modern Finance
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Industrial Application of Deep Neural Network for Aluminum Casting Defect Detection in Case of Unbalanced Dataset
Autorzy:
Awtoniuk, Michał
Majerek, Dariusz
Myziak, Artur
Gajda, Cyprian
Powiązania:
https://bibliotekanauki.pl/articles/2204946.pdf
Data publikacji:
2022
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
machine learning
deep neural network
classification
casting defect
casting defect detection
Opis:
We have developed a deep neural network for casting defect detection. The approach is original because it assumes the use of data related to the casting manufacturing process, i.e. measurement signals from the casting machine, rather than data describing the finished casting, e.g. images. The defects are related to the production of car engine heads made of silumin. In the current research we focused on the detection of defects related to the leakage of the casting. The data came from production plant in Poland. The dataset was unbalanced. It included nearly 38,500 observations, of which only 4% described a leak event. The work resulted in a deep network consisting of 22 layers. We assessed the classification accuracy using a ROC curve, an AUC index and a confusion matrix. The AUC value was 0.97 and 0.949 for the learning and testing dataset, respectively. The model allowed for an ex-post analysis of the casting process. The analysis was based on Shapley values. This makes it possible not only to detect the occurrence of a defect but also to give potential reasons for the appearance of a casting leak.
Źródło:
Advances in Science and Technology. Research Journal; 2022, 16, 5; 120--128
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Explainable deep neural network-based analysis on intrusion-detection systems
Autorzy:
Pande, Sagar Dhanraj
Khamparia, Aditya
Powiązania:
https://bibliotekanauki.pl/articles/27312883.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
IDS
deep neural network
explainable AI
NSL-KDD
local explainability
global explainability
Opis:
The research on intrusion-detection systems (IDSs) has been increasing in recent years. Particularly, this research widely utilizes machine-learning concepts, and it has proven that these concepts are effective with IDSs – particularly, deep neural network-based models have enhanced the rates of the detection of IDSs. In the same instance, these models are turning out to be very complex, and users are unable to track down explanations for the decisions that are made; this indicates the necessity of identifying the explanations behind those decisions to ensure the interpretability of the framed model. In this aspect, this article deals with a proposed model that can explain the obtained predictions. The proposed framework is a combination of a conventional IDS with the aid of a deep neural network and the interpretability of the model predictions. The proposed model utilizes Shapley additive explanations (SHAPs) that mixes the local explainability as well as the global explainability for the enhancement of interpretations in the case of IDS. The proposed model was implemented by using popular data sets (NSL-KDD and UNSW-NB15), and the performance of the framework was evaluated by using their accuracy. The framework achieved accuracy levels of 99.99 and 99.96%, respectively. The proposed framework can identify the top-4 features using local explainability and the top-20 features using global explainability.
Źródło:
Computer Science; 2023, 24 (1); 97--111
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Goal - oriented conversational bot for employment domain
Autorzy:
Drozda, Paweł
Żmijewski, Tomasz
Osowski, Maciej
Krasnodębska, Aleksandra
Talun, Arkadiusz
Powiązania:
https://bibliotekanauki.pl/articles/22615524.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
chatbot
Deep Q Network
DQN
goal
oriented bot
Natural Language Processing
NLP
Opis:
This paper focuses of the implementation of the goal – oriented chatbot in order to prepare virtual resumes of candidates for job position. In particular the study was devoted to testing the feasibility of using Deep Q Networks (DQN) to prepare an effective chatbot conversation flow with the final system user. The results of the research confirmed that the use of the DQN model in the training of the conversational system allowed to increase the level of success, measured as the acceptance of the resume by the recruiter and the finalization of the conversation with the bot. The success rate increased from 10% to 64% in experimental environment and from 15% to 45% in production environment. Moreover, DQN model allowed the conversation to be shortened by an average of 4 questions from 11 to 7.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2023, 26(1); 111--123
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Graph-based segmentation with homogeneous hue and texture vertices
Autorzy:
Ngo, Lua
Han, Jae-Ho
Powiązania:
https://bibliotekanauki.pl/articles/2033896.pdf
Data publikacji:
2021
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
image segmentation
deep neural network
electron microscopy
optical coherence tomography
pattern recognition
Opis:
This work presents an automated segmentation method, based on graph theory, which processes superpixels that exhibit spatially similarities in hue and texture pixel groups, rather than individual pixels. The graph shortest path includes a chain of neighboring superpixels which have minimal intensity changes. This method reduces graphics computational complexity because it provides large decreases in the number of vertices as the superpixel size increases. For the starting vertex prediction, the boundary pixel in first column which is included in this starting vertex is predicted by a trained deep neural network formulated as a regression task. By formulating the problem as a regression scheme, the computational burden is decreased in comparison with classifying each pixel in the entire image. This feasibility approach, when applied as a preliminary study in electron microscopy and optical coherence tomography images, demonstrated high measures of accuracy: 0.9670 for the electron microscopy image and 0.9930 for vitreous/nerve-fiber and inner-segment/outer-segment layer segmentations in the optical coherence tomography image.
Źródło:
Optica Applicata; 2021, 51, 4; 541-549
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Position-encoding convolutional network to solving connected text CAPTCHA
Autorzy:
Qing, Ke
Zhang, Rong
Powiązania:
https://bibliotekanauki.pl/articles/2147133.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep neural network
position encoding CNN
text-based CAPTCHA recognition
character recognition
Opis:
Text-based CAPTCHA is a convenient and effective safety mechanism that has been widely deployed across websites. The efficient end-to-end models of scene text recognition consisting of CNN and attention-based RNN show limited performance in solving text-based CAPTCHAs. In contrast with the street view image and document, the character sequence in CAPTCHA is non-semantic. The RNN loses its ability to learn the semantic context and only implicitly encodes the relative position of extracted features. Meanwhile, the security features, which prevent characters from segmentation and recognition, extensively increase the complexity of CAPTCHAs. The performance of this model is sensitive to different CAPTCHA schemes. In this paper, we analyze the properties of the text-based CAPTCHA and accordingly consider solving it as a highly position-relative character sequence recognition task. We propose a network named PosConv to leverage the position information in the character sequence without RNN. PosConv uses a novel padding strategy and modified convolution, explicitly encoding the relative position into the local features of characters. This mechanism of PosConv makes the extracted features from CAPTCHAs more informative and robust. We validate PosConv on six text-based CAPTCHA schemes, and it achieves state-of-the-art or competitive recognition accuracy with significantly fewer parameters and faster convergence speed.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 121--133
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep Neural Network for Supervised Single-Channel Speech Enhancement
Autorzy:
Saleem, Nasir
Irfan Khattak, Muhammad
Ali, Muhammad Yousaf
Shafi, Muhammad
Powiązania:
https://bibliotekanauki.pl/articles/177497.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
deep neural network
intelligibility
speech enhancement
speech quality
supervised learning
Wiener filtering
Opis:
Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.
Źródło:
Archives of Acoustics; 2019, 44, 1; 3-12
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Minimization of Energy and Service Latency Computation Offloading using Neural Network in 5G NOMA System
Autorzy:
Suprith, P. G.
Ahmed, Mohammed Riyaz
Powiązania:
https://bibliotekanauki.pl/articles/27311932.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
Mobile edge computing
Deep Q Network Algorithm
latency optimized
computation offloading
5G
Opis:
The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 661--667
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification of Parkinsons disease in brain MRI images using Deep Residual Convolutional Neural Network (DRCNN)
Autorzy:
Praneeth, Puppala
Sathvika, Majety
Kommareddy, Vivek
Sarath, Madala
Mallela, Saran
Vani, K. Suvarna
Chkrabarti, Prasun
Powiązania:
https://bibliotekanauki.pl/articles/30148251.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
Parkinson’s disease
Deep Residual Convolutional Neural Network
deep learning
health control
Opis:
In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, the authors propose a technique to classify Parkinson’s disease by MRI brain images. Initially, the input data is normalized using the min-max normalization method, and then noise is removed from the input images using a median filter. The Binary Dragonfly algorithm is then used to select features. In addition, the Dense-UNet technique is used to segment the diseased part from brain MRI images. The disease is then classified as Parkinson's disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with the Enhanced Whale Optimization Algorithm (EWOA) to achieve better classification accuracy. In this work, the Parkinson's Progression Marker Initiative (PPMI) public dataset for Parkinson's MRI images is used. Indicators of accuracy, sensitivity, specificity and precision are used with manually collected data to evaluate the effectiveness of the proposed methodology.
Źródło:
Applied Computer Science; 2023, 19, 2; 125-146
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks
Autorzy:
Chu, J. L.
Krzyżak, A.
Powiązania:
https://bibliotekanauki.pl/articles/91650.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
neural networks
belief networks
convolutional neural networks
artificial neural networks
Deep Belief Network
generative model
Opis:
Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 5-19
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Applications of generative models with a latent observation subspace in vibrodiagnostics
Autorzy:
Puchalski, Andrzej
Komorska, Iwona
Powiązania:
https://bibliotekanauki.pl/articles/27313835.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN
Tematy:
vibration signal
deep neural network
generative adversarial network
GAN model
synthetic subspace
sygnał wibracyjny
głęboka sieć neuronowa
GAN
wibrodiagnostyka
Opis:
The vibration signal is one of the most essential diagnostic signals, the analysis of which allows for determining the dynamic state of the monitored machine set. In the era of cyber-physical industrial systems, making diagnostic decisions involves the study of large databases from previous registers and data downloaded from machines in real-time. However, the recorded signals mainly concern the operational status of the monitored object. Insufficient training data regarding failure states hinders the operation of classification algorithms. Progress in machine learning has created a new avenue for the advancement of diagnostic methods based on models. These methods now have the capability to produce signals through random sampling from a hidden space or generate fresh instances of input data from noise. The article suggests the use of a Generative Adversarial Network (GAN) model as a tool to create synthetic measurement observations for vibration monitoring. The effectiveness of the synthetic data generation algorithm was verified on the example of the vibration signal recorded during tests of the drive system of a motor vehicle.
Źródło:
Diagnostyka; 2023, 24, 4; art. no. 2023413
1641-6414
2449-5220
Pojawia się w:
Diagnostyka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade
Autorzy:
Jasti, Sireesha
Kumar, G.V.S. Raj
Powiązania:
https://bibliotekanauki.pl/articles/2200961.pdf
Data publikacji:
2023
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
deep learning network
feedback artificial tree
natural language processing (NLP)
rider optimization algorithm
sentiment grade classification
Opis:
Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.
Źródło:
Journal of Telecommunications and Information Technology; 2023, 1; 37--50
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
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