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Wyświetlanie 1-15 z 202
Tytuł:
Towards ensuring software interoperability between deep learning frameworks
Autorzy:
Lee, Youn Kyu
Park, Seong Hee
Lim, Min Young
Lee, Soo-Hyun
Jeong, Jongwook
Powiązania:
https://bibliotekanauki.pl/articles/23944833.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep learning
interoperability
validation
verification
deep learning framework
model conversion
Opis:
With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 4; 215--228
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Improving accuracy of detecting dangerous objects with deep learning
Poprawa skuteczności wykrycia niebezpiecznych obiektów przy użyciu technik deep learning
Autorzy:
Zacniewski, A.
Powiązania:
https://bibliotekanauki.pl/articles/315763.pdf
Data publikacji:
2016
Wydawca:
Instytut Naukowo-Wydawniczy "SPATIUM"
Tematy:
detecting dangerous objects
deep learning
detekcja niebezpiecznych obiektów
technika deep learning
Opis:
In this article, the problem of detecting dangerous objects with deep learning is presented. Convolutional Neural Networks are created with Python language ecosystem (Theano and Keras libraries), and then trained with different number of layers and different parameters. Accuracy of detection dangerous objects for artificial Neural Network with smaller number of layers is computed and obtained result is improved with deep learning. CIFAR-10 dataset is used due to useful classes included.
W artykule przedstawiono problem detekcji niebezpiecznych obiektów przy użyciu technik deep learning. Konwolucyjne sieci neuronowe tworzone są przy pomocy bibliotek języka Python takich jak Keras i Theano, a następnie trenowane są przy różnej liczbie warstw i z różnymi parametrami. Skuteczność detekcji niebezpiecznych obiektów dla małej liczby warstw sztucznej sieci neuronowej jest obliczana, a uzyskany wynik jest ulepszany przy użyciu technik deep learning. Zbiór danych CIFAR-10 został wykorzystany w badaniach z powodu dużej użyteczności występujących w nim klas.
Źródło:
Autobusy : technika, eksploatacja, systemy transportowe; 2016, 17, 12; 513-516
1509-5878
2450-7725
Pojawia się w:
Autobusy : technika, eksploatacja, systemy transportowe
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep reinforcement learning overview of the state of the art
Autorzy:
Fenjiro, Y.
Benbrahim, H.
Powiązania:
https://bibliotekanauki.pl/articles/384788.pdf
Data publikacji:
2018
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
reinforcement learning
deep learning
convolutional network
recurrent network
deep reinforcement learning
Opis:
Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-toend framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (DRL). In this paper, we will go through the development Timeline of RL and DL technologies, describing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-ofthe- art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization and Actor-Critic), then, giving an outline of current challenges and real-world applications, along with the hardware and frameworks used. In the end, we will discuss some potential research directions in the field of deep RL, for which we have great expectations that will lead to a real human level of intelligence.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 3; 20-39
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel deep neural network that uses space-time features for tracking and recognizing a moving object
Autorzy:
Chang, O.
Constante, P.
Gordon, A.
Singaña, M.
Powiązania:
https://bibliotekanauki.pl/articles/91702.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep architectures
deep learning
artificial vision
Opis:
This work proposes a deep neural net (DNN) that accomplishes the reliable visual recognition of a chosen object captured with a webcam and moving in a 3D space. Autoencoding and substitutional reality are used to train a shallow net until it achieves zero tracking error in a discrete ambient. This trained individual is set to work in a real world closed loop system where images coming from a webcam produce displacement information for a moving region of interest (ROI) inside the own image. This loop gives rise to an emergent tracking behavior which creates a self-maintain flow of compressed space-time data. Next, short term memory elements are set to play a key role by creating new representations in terms of a space-time matrix. The obtained representations are delivery as input to a second shallow network which acts as ”recognizer”. A noise balanced learning method is used to fast train the recognizer with real-world images, giving rise to a simple and yet powerful robotic eye, with a slender neural processor that vigorously tracks and recognizes the chosen object. The system has been tested with real images in real time.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 2; 125-136
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Interferogram blind denoising using deep residual learning for phase-shifting interferometry
Autorzy:
Xu, Xiaoqing
Xie, Ming
Chen, Song
Ji, Ying
Wang, Yawei
Powiązania:
https://bibliotekanauki.pl/articles/2060681.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
interferogram denoising
deep learning
interferometry
Opis:
The interferogram containing the noises often affects the accuracy of phase retrieval, leading to the degradation of the phase imaging quality. To address this issue, a new interferogram blind denoising (IBD) method based on deep residual learning is proposed. In the presence of unknown noise levels, during the training, the deep residual convolutional neural networks (DRCNN) in the IBD approach is able to remove the latent clean interferogram implicitly, and then gradually establish the residual mapping relation in the pixel-level between the interferogram and the noises. With a well-trained DRCNN model, this algorithm can deal not only with the single-frame interferogram efficiently but also with the multi-frame phase-shifted interferograms collaboratively, while effectively retaining interferogram features related to phase retrieval. Simulation and experimental results demonstrate the feasibility and applicability of the proposed IBD method.
Źródło:
Optica Applicata; 2022, 52, 1; 101--116
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Word prediction in computational historical linguistics
Autorzy:
Dekker, Peter
Zuidema, Willem
Powiązania:
https://bibliotekanauki.pl/articles/1818886.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:
computational historical linguistics
machine learning
deep learning
Opis:
In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.
Źródło:
Journal of Language Modelling; 2020, 8, 2; 295--336
2299-856X
2299-8470
Pojawia się w:
Journal of Language Modelling
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Word prediction in computational historical linguistics
Autorzy:
Dekker, Peter
Zuidema, Willem
Powiązania:
https://bibliotekanauki.pl/articles/1818890.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Podstaw Informatyki PAN
Tematy:
computational historical linguistics
machine learning
deep learning
Opis:
In this paper, we investigate how the prediction paradigm from machine learning and Natural Language Processing (NLP) can be put to use in computational historical linguistics. We propose word prediction as an intermediate task, where the forms of unseen words in some target language are predicted from the forms of the corresponding words in a source language. Word prediction allows us to develop algorithms for phylogenetic tree reconstruction, sound correspondence identification and cognate detection, in ways close to attested methods for linguistic reconstruction. We will discuss different factors, such as data representation and the choice of machine learning model, that have to be taken into account when applying prediction methods in historical linguistics. We present our own implementations and evaluate them on different tasks in historical linguistics.
Źródło:
Journal of Language Modelling; 2020, 8, 2; 295--336
2299-856X
2299-8470
Pojawia się w:
Journal of Language Modelling
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of a neural network structure for identifying begin-end points in the assembly process
Autorzy:
Kutschenreiter-Praszkiewicz, Izabela
Powiązania:
https://bibliotekanauki.pl/articles/24084694.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
deep learning
assembly
begin-end point
Opis:
The paper presents an approach to video-based assembly analysis using machine learning. A neural network is one of the machine learning methods that is widely studied in many engineering fields. The purpose of this paper is to develop a deep neural network structure for identifying begin-end points for a selected component assembly process. A neural network structure that effectively identifies begin-end points is proposed and an example from industry is presented. The proposed approach can prove useful in the assembly process analysis.
Źródło:
Journal of Machine Engineering; 2023, 23, 2; 100-109
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Phase retrieval without phase unwrapping for white blood cells in deep-learning phase-shifting digital holography
Autorzy:
Jin, Shuyang
Xu, Xiaoqing
Chen, Jili
Ni, Yudan
Powiązania:
https://bibliotekanauki.pl/articles/2202768.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
digital holography
phase retrieval
deep learning
Opis:
Phase retrieval and phase unwrapping are the two important problems for enabling quantitative phase imaging of cells in phase-shifting digital holography. To simultaneously cope with these two problems, a deep-learning phase-shifting digital holography method is proposed in this paper. The proposed method can establish the continuous mapping function of the interferogram to the ground-truth phase using the end-to-end convolutional neural network. With a well-trained deep convolutional neural network, this method can retrieve the phase from one-frame blindly phase-shifted interferogram, without phase unwrapping. The feasibility and applicability of the proposed method are verified by the simulation experiments of the microsphere and white blood cells, respectively. This method will pave the way to the quantitative phase imaging of biological cells with complex substructures.
Źródło:
Optica Applicata; 2023, 53, 1; 127--140
0078-5466
1899-7015
Pojawia się w:
Optica Applicata
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time face mask detection in mass gatherings to reduce Covid-19 spread
Autorzy:
Soner, Swapnil
Litoriya, Ratnesh
Khatri, Ravi
Hussain, Ali Asgar
Pagrey, Shreyas
Kushwaha, Sunil Kumar
Powiązania:
https://bibliotekanauki.pl/articles/27314233.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
Covid
machine learning
face mask detection
Deep Learning
Opis:
The Covid 19 (coronavirus) pandemic has become one of the most lethal health crises worldwide. This virus gets transmitted from a person by respiratory droplets when they sneeze or when they speak. According to leading and well‐known scientists, wearing face masks and maintain‐ ing six feet of social distance are the most substantial protections to limit the virus’s spread. In the proposed model we have used the Convolutional Neural Network (CNN) algorithm of Deep Learning (DL) to ensure efficient real‐time mask detection. We have divided the system into two parts—1. Train Face Mask Detector 2. Apply Face Mask Detector—for better understanding. This is a real‐ time application that is used to discover or detect the person who is wearing a mask at the proper position or not, with the help of camera detection. The system has achieved an accuracy of 99% after being trained with the dataset, which contains around 1376 images of width and height 224×224 and also gives the alarm beep message after the detection of no mask or improper mask usage in a public place.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 1; 51--58
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Navigation strategy for mobile robot based on computer vision and YOLOv5 network in the unknown environment
Autorzy:
Bui, Thanh-Lam
Tran, Ngoc-Tien
Powiązania:
https://bibliotekanauki.pl/articles/30148249.pdf
Data publikacji:
2023
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
mobile robot
navigation
deep learning
computer vision
Opis:
The capacity to navigate effectively in complex environments is a crucial prerequisite for mobile robots. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. When the light illumination increases from 300 lux to 1000 lux, the reliability of the recognition model on different objects also improves, from about 75% to 98%, respectively. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation.
Źródło:
Applied Computer Science; 2023, 19, 2; 82-95
1895-3735
2353-6977
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A CNN Approach to Central Retinal Vein Occlusion Detection
Autorzy:
Bala, Jayanthi Rajee
Sindha, Mohamed Mansoor Roomi
Sahayam, Jency
Govindharaj, Praveena
Rakesh, Karthika Priya
Powiązania:
https://bibliotekanauki.pl/articles/27311911.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
Blood vessels
segmentation
Features
CRVO
deep learning
Opis:
In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 3; 565--570
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A novel approach of voterank-based knowledge graph for improvement of multi-attributes influence nodes on social networks
Autorzy:
Pham, Hai Van
Duong, Pham Van
Tran, Dinh Tuan
Lee, Joo-Ho
Powiązania:
https://bibliotekanauki.pl/articles/23944825.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
video surveillance
deep learning
moving object detection
Opis:
Recently, measuring users and community influences on social media networks play significant roles in science and engineering. To address the problems, many researchers have investigated measuring users with these influences by dealing with huge data sets. However, it is hard to enhance the performances of these studies with multiple attributes together with these influences on social networks. This paper has presented a novel model for measuring users with these influences on a social network. In this model, the suggested algorithm combines Knowledge Graph and the learning techniques based on the vote rank mechanism to reflect user interaction activities on the social network. To validate the proposed method, the proposed method has been tested through homogeneous graph with the building knowledge graph based on user interactions together with influences in realtime. Experimental results of the proposed model using six open public data show that the proposed algorithm is an effectiveness in identifying influential nodes.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 165--180
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Brief Review of Recent Developments in the Integration of Deep Learning with GIS
Autorzy:
Mohan, Shyama
Giridhar, M.V.S.S
Powiązania:
https://bibliotekanauki.pl/articles/2055781.pdf
Data publikacji:
2022
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
GIS
integration
classification
remote sensing
Opis:
The interaction of Deep Learning (DL) methods with Geographical Information System (GIS) provides the opportunity to obtain new insights into environmental processes through the spatial, temporal and spectral resolutions as well as data integration. The two technologies may be connected to form a dynamic system that is incredibly well adapted to the evaluation of environmental conditions through the interrelationships of texture, size, pattern, and process. This perspective has acquired popularity in multiple disciplines. GIS is significantly dependant on processors, particularly for 3D calculations, map rendering, and route calculation whereas DL can process huge amounts of data. DL has received a lot of attention recently as a technology with a plethora of promising results. Furthermore, the growing use of DL methods in a variety of disciplines, including GIS, is evident. This study tries to provide a brief overview of the use of DL methods in GIS. This paper introduces the essential DL concepts relevant to GIS, the majority of which have been published in recent years. This research explores remote sensing applications and technologies in areas such as mapping, hydrological modelling, disaster management, and transportation route planning. Finally, conclusions on contemporary framework methodologies and suggestions for further studies are provided.
Źródło:
Geomatics and Environmental Engineering; 2022, 16, 2; 21--38
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Characterization of symbolic rules embedded in deep DIMLP networks : a challenge to transparency of deep learning
Autorzy:
Bologna, G.
Hayashi, Y.
Powiązania:
https://bibliotekanauki.pl/articles/91545.pdf
Data publikacji:
2017
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
ensemble
Deep Learning
rule extraction
feature detectors
Opis:
Rule extraction from neural networks is a fervent research topic. In the last 20 years many authors presented a number of techniques showing how to extract symbolic rules from Multi Layer Perceptrons (MLPs). Nevertheless, very few were related to ensembles of neural networks and even less for networks trained by deep learning. On several datasets we performed rule extraction from ensembles of Discretized Interpretable Multi Layer Perceptrons (DIMLP), and DIMLPs trained by deep learning. The results obtained on the Thyroid dataset and the Wisconsin Breast Cancer dataset show that the predictive accuracy of the extracted rules compare very favorably with respect to state of the art results. Finally, in the last classification problem on digit recognition, generated rules from the MNIST dataset can be viewed as discriminatory features in particular digit areas. Qualitatively, with respect to rule complexity in terms of number of generated rules and number of antecedents per rule, deep DIMLPs and DIMLPs trained by arcing give similar results on a binary classification problem involving digits 5 and 8. On the whole MNIST problem we showed that it is possible to determine the feature detectors created by neural networks and also that the complexity of the extracted rulesets can be well balanced between accuracy and interpretability.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2017, 7, 4; 265-286
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 202

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