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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
Tytuł:
Deep learning based road recognition for intelligent suspension systems
Autorzy:
Sun, Jinwei
Cong, Jingyu
Powiązania:
https://bibliotekanauki.pl/articles/2055054.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
intelligent suspension system
deep learning
road recognition
Opis:
This paper presents a deep learning-based road recognition strategy for advanced suspension systems. A four-quarter suspension model with a magnetorheological (MR) damper is developed, and four typical road images with corresponding roughness data are collected. A back-propagation neural network based autoencoder and Convolutional Neural Networks (CNN) are utilized to form the deep learning structure. By utilizing the multi-object genetic algorithm, the optimal parameters can be obtained, and the control current can be adaptively adjusted. Simulation results indicate that the designed structure can identify the road type accurately, and the recognition-based control strategy can improve the suspension performance effectively.
Źródło:
Journal of Theoretical and Applied Mechanics; 2021, 59, 3; 493--508
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Detrimental Starfish Detection on Embedded System: A Case Study of YOLOv5 Deep Learning Algorithm and TensorFlow Lite framework
Autorzy:
Toan, Nguyen Quoc
Powiązania:
https://bibliotekanauki.pl/articles/2086221.pdf
Data publikacji:
2022
Wydawca:
Politechnika Lubelska. Instytut Informatyki
Tematy:
deep learning
computer vision
YOLO
embedded system
Opis:
There is a great range of spectacular coral reefs in the ocean world. Unfortunately, they are in jeopardy, due to an overabundance of one specific starfish called the coral-eating crown-of-thorns starfish (or COTS). This article provides research to deliver innovation in COTS control. Using a deep learning model based on the You Only Look Once version 5 (YOLOv5) deep learning algorithm on an embedded device for COTS detection. It aids professionals in optimizing their time, resources, and enhances efficiency for the preservation of coral reefs worldwide. As a result, the performance over the algorithm was outstanding with Precision: 0.93 - Recall: 0.77 - F1score: 0.84.
Źródło:
Journal of Computer Sciences Institute; 2022, 23; 105--111
2544-0764
Pojawia się w:
Journal of Computer Sciences Institute
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Moving object detection for complex scenes by merging BG modeling and deep learning method
Autorzy:
Lin, Chih-Yang
Huang, Han-Yi
Lin, Wei-Yang
Ng, Hui-Fuang
Muchtar, Kahlil
Nurdin, Nadhila
Powiązania:
https://bibliotekanauki.pl/articles/23944823.pdf
Data publikacji:
2023
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
video surveillance
deep learning
moving object detection
Opis:
In recent years, many studies have attempted to use deep learning for moving object detection. Some research also combines object detection methods with traditional background modeling. However, this approach may run into some problems with parameter settings and weight imbalances. In order to solve the aforementioned problems, this paper proposes a new way to combine ViBe and Faster-RCNN for moving object detection. To be more specific, our approach is to confine the candidate boxes to only retain the area containing moving objects through traditional background modeling. Furthermore, in order to make the detection able to more accurately filter out the static object, the probability of each region proposal then being retained. In this paper, we compare four famous methods, namely GMM and ViBe for the traditional methods, and DeepBS and SFEN for the deep learning-based methods. The result of the experiment shows that the proposed method has the best overall performance score among all methods. The proposed method is also robust to the dynamic background and environmental changes and is able to separate stationary objects from moving objects. Especially the overall F-measure with the CDNET 2014 dataset (like in the dynamic background and intermittent object motion cases) was 0,8572.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 151--163
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Exploring convolutional auto-encoders for representation learning on networks
Autorzy:
Nerurkar, Pranav Ajeet
Chandane, Madhav
Bhirud, Sunil
Powiązania:
https://bibliotekanauki.pl/articles/305489.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
network representation learning
deep learning
graph convolutional neural networks
Opis:
A multitude of important real-world or synthetic systems possess network structures. Extending learning techniques such as neural networks to process such non-Euclidean data is therefore an important direction for machine learning re- search. However, this domain has received comparatively low levels of attention until very recently. There is no straight-forward application of machine learning to network data, as machine learning tools are designed for i:i:d data, simple Euclidean data, or grids. To address this challenge, the technical focus of this dissertation is on the use of graph neural networks for network representation learning (NRL); i.e., learning the vector representations of nodes in networks. Learning the vector embeddings of graph-structured data is similar to embedding complex data into low-dimensional geometries. After the embedding process is completed, the drawbacks associated with graph-structured data are overcome. The current inquiry proposes two deep-learning auto-encoder-based approaches for generating node embeddings. The drawbacks in such existing auto-encoder approaches as shallow architectures and excessive parameters are tackled in the proposed architectures by using fully convolutional layers. Extensive experiments are performed on publicly available benchmark network datasets to highlight the validity of this approach.
Źródło:
Computer Science; 2019, 20 (3); 273-288
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative Experimental Investigation and Application of Five Classic Pre-Trained Deep Convolutional Neural Networks via Transfer Learning for Diagnosis of Breast Cancer
Autorzy:
Nogay, Hidir Selcuk
Akinci, Tahir Cetin Akinci
Yilmaz, Musa
Powiązania:
https://bibliotekanauki.pl/articles/2023314.pdf
Data publikacji:
2021
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
breast cancer
classification
deep learning
DCNN
transfer learning
diagnosis
Opis:
In this study, for the diagnosis and classification of breast cancer, we used and applied five classical pre-trained deep convolutional neural network models (DCNN) which have proven successful many times in different fields (ResNet-18, AlexNet, GoogleNet and SuffleNet). To make pre-trained DCNN models suitable for the purpose of our study, we updated some layers according to the new situation by using the transfer learning technique. We did not change the weights of all layers used in these five pre-trained DCNN models. Instead, we just gave new weights to the new layers so that new layers adapt faster to emerging new DCNN models. With these five pre-trained DCNN models, we have realized a quadruple classification as "cancer", "normal", "actionable" and "benign", and a binary classification as "actionable + cancer" and "normal + benign". With these two separate classification and diagnosis studies, we have carried out comparative experimental examination and analysis of pre-trained DCNN models for breast cancer diagnosis. In the study, it was concluded that successful results can be achieved with pre-trained DCNN models without extra time-consuming procedures such as feature extraction, and DCNN can perform quite successfully in cancer diagnosis and image comment.
Źródło:
Advances in Science and Technology. Research Journal; 2021, 15, 3; 1-8
2299-8624
Pojawia się w:
Advances in Science and Technology. Research Journal
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks
Autorzy:
Prajapati, Hardik K.
Joshi, Rutvij
Powiązania:
https://bibliotekanauki.pl/articles/2200710.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
Deep learning
Convolutional Neural Network (CNN)
LEACH
Opis:
Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.We used a combination of LEACH and deep learning to extend the network life of the WSN in this study. In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 799--805
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A model of continual and deep learning for aspect based in sentiment analysis
Autorzy:
López, Dionis
Artigas-Fuentes, Fernando
Powiązania:
https://bibliotekanauki.pl/articles/27314219.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
continual learning
deep learning
catas
trophic forgetting
sentiment analysis
Opis:
Sentiment analysis is a useful tool in several social and business contexts. Aspect sentiment classification is a subtask in sentiment analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different deep learning models that have been proposed to solve aspect sen‐ timent classification focus on a specific domain such as restaurant, hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The continual learn‐ ing approach with neural networks has been used to solve aspect classification in multiple domains. However, avoiding low, aspect classification performance in contin‐ ual learning is challenging. As a consequence, potential neural network weight shifts in the learning process in different domains or datasets. In this paper, a novel aspect sentiment classification approach is proposed. Our approach combines a trans‐ former deep learning technique with a continual learning algorithm in different domains. The input layer used is the pretrained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 % F1‐macro. Our results improve other approaches from the state‐of-the-art.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 1; 3--12
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Python Machine Learning. Dry Beans Classification Case
Autorzy:
Słowiński, Grzegorz
Powiązania:
https://bibliotekanauki.pl/articles/50091919.pdf
Data publikacji:
2024-09
Wydawca:
Warszawska Wyższa Szkoła Informatyki
Tematy:
machine learning
deep learning
data dimension reduction
activation function
Opis:
A dataset containing over 13k samples of dry beans geometric features was analyzed using machine learning (ML) and deep learning (DL) techniques with the goal to automatically classify the bean species. Performance in terms of accuracy, train and test time was analyzed. First the original dataset was reduced to eliminate redundant features (too strongly correlated and echoing others). Then the dataset was visualized and analyzed with a few shallow learning techniques and simple artificial neural network. Cross validation was used to check the learning process repeatability. Influence of data preparation (dimension reduction) on shallow learning techniques were observed. In case of Multilayer Perceptron 3 activation functions were tried: ReLu, ELU and sigmoid. Random Forest appeared to be the best model for dry beans classification task reaching average accuracy reaching 92.61% with reasonable train and test times.
Źródło:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2024, 18, 30; 7-26
1896-396X
2082-8349
Pojawia się w:
Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation of bone structures with the use of deep learning techniques
Autorzy:
Krawczyk, Zuzanna
Starzyński, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2128158.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
semantic segmentation
U-net
FCN
ResNet
computed tomography
technika deep learning
głęboka nauka
segmentacja semantyczna
tomografia komputerowa
Opis:
The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136751, 1--8
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Update on the study of Alzheimer’s disease through artificial intelligence techniques
Autorzy:
Garea-Llano, Eduardo
Powiązania:
https://bibliotekanauki.pl/articles/27314235.pdf
Data publikacji:
2023
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
Alzheimer's disease
detection
progression
artificial intelligence
deep learning
Opis:
Alzheimer’s disease is the most common form of dementia that can cause a brain neurological disorder with progressive memory loss as a result of brain cell damage. Prevention and treatment of disease is a key challenge in today’s aging society. Accurate diagnosis of Alzheimer’s disease plays an important role in patient management, especially in the early stages of the disease, because awareness of risk allows patients to undergo preventive measures even before irreversible brain damage occurs. Over the years, techniques such as statistical modeling or machine learning algorithms have been used to improve understanding of this condition. The objective of the work is the study of the methods of detection and progression of Alzheimer’s disease through artificial intelligence techniques that have been proposed in the last three years. The methodology used was based on the search, selection, review, and analysis of the state of the art and the most current articles published on the subject. The most representative works were analyzed, which allowed proposing a taxonomic classification of the studied methods and on this basis a possible solution strategy was proposed within the framework of the project developed by the Cuban Center for Neurosciences based on the conditions more convenient in terms of cost and effectiveness and the most current trends based on the use of artificial intelligence techniques.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2023, 17, 2; 51--60
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Skin Lesion Analysis Toward Melanoma Detection Using Deep Learning Techniques
Autorzy:
Sherif, Fatma
Mohamed, Wael A.
Mohra, A.S.
Powiązania:
https://bibliotekanauki.pl/articles/226719.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
melanoma
skin cancer
convolutional neural network
deep learning
Opis:
In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set. The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 597-602
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparative study of CNN and LSTM for opinion mining in long text
Autorzy:
Yousf, Siham
Rhanoui, Maryem
Mounia, Mikram
Powiązania:
https://bibliotekanauki.pl/articles/1837369.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
deep learning
long text opinion mining
CNN
LSTM
Opis:
The digital revolution has encouraged many companies to set up new strategic and operational mechanisms to supervise the flow of information published about them on the Web. Press coverage analysis is a part of sentiment analysis that allows companies to discover the opinion of the media concerning their activities, products and services. It is an important research area, since it involves the opinion of informed public such as journalists, who may influence the opinion of their readers. However, from an implementation perspective, the analysis of the opinion from media coverage encounters many challenges. In fact, unlike social networks, the Media coverage is a set of large textual documents written in natural language. The training base being huge, it is necessary to adopt large-scale processing techniques like Deep Learning to analyze their content. To guide researchers to choose between one of the most commonly used models CNN and LSTM, we compare and apply both models for opinion mining from long text documents using real datasets.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 3; 50-55
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation of bone structures with the use of deep learning techniques
Autorzy:
Krawczyk, Zuzanna
Starzyński, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/2173574.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
semantic segmentation
U-net
FCN
ResNet
computed tomography
technika deep learning
głęboka nauka
segmentacja semantyczna
tomografia komputerowa
Opis:
The paper is focused on automatic segmentation task of bone structures out of CT data series of pelvic region. The authors trained and compared four different models of deep neural networks (FCN, PSPNet, U-net and Segnet) to perform the segmentation task of three following classes: background, patient outline and bones. The mean and class-wise Intersection over Union (IoU), Dice coefficient and pixel accuracy measures were evaluated for each network outcome. In the initial phase all of the networks were trained for 10 epochs. The most exact segmentation results were obtained with the use of U-net model, with mean IoU value equal to 93.2%. The results where further outperformed with the U-net model modification with ResNet50 model used as the encoder, trained by 30 epochs, which obtained following result: mIoU measure – 96.92%, “bone” class IoU – 92.87%, mDice coefficient – 98.41%, mDice coefficient for “bone” – 96.31%, mAccuracy – 99.85% and Accuracy for “bone” class – 99.92%.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136751
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Towards sustainable and intelligent machining: energy footprint and tool condition monitoring for media-assisted processes
Autorzy:
Dogan, Hakan
Jones, Llyr
Hall, Stephanie
Shokrani, Alborz
Powiązania:
https://bibliotekanauki.pl/articles/24084657.pdf
Data publikacji:
2023
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
machining
deep learning
tool condition monitoring
energy footprint
Opis:
Reducing energy consumption is a necessity towards achieving the goal of net-zero manufacturing. In this paper, the overall energy footprint of machining Ti-6Al-4V using various cooling/lubrication methods is investigated taking the embodied energy of cutting tools and cutting fluids into account. Previous studies concentrated on reducing the energy consumption associated with the machine tool and cutting fluids. However, the investigations in this study show the significance of the embodied energy of cutting tool. New cooling/lubrication methods such as WS2-oil suspension can reduce the energy footprint of machining through extending tool life. Cutting tools are commonly replaced early before reaching their end of useful life to prevent damage to the workpiece, effectively wasting a portion of the embodied energy in cutting tools. A deep learning method is trained and validated to identify when a tool change is required based on sensor signals from a wireless sensory toolholder. The results indicated that the network is capable of classifying over 90% of the tools correctly. This enables capitalising on the entirety of a tool’s useful life before replacing the tool and thus reducing the overall energy footprint of machining processes.
Źródło:
Journal of Machine Engineering; 2023, 23, 2; 16--40
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Tomato disease detection model based on densenet and transfer learning
Autorzy:
Bakr, Mahmoud
Abdel-Gaber, Sayed
Nasr, Mona
Hazman, Maryam
Powiązania:
https://bibliotekanauki.pl/articles/2097440.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
leaf disease detection
convolutional neural network
deep learning
transfer learning
Opis:
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Źródło:
Applied Computer Science; 2022, 18, 2; 56--70
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An automated driving strategy generating method based on WGAIL–DDPG
Autorzy:
Zhang, Mingheng
Wan, Xing
Gang, Longhui
Lv, Xinfei
Wu, Zengwen
Liu, Zhaoyang
Powiązania:
https://bibliotekanauki.pl/articles/2055167.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
automated driving system
deep learning
deep reinforcement learning
imitation learning
deep deterministic policy gradient
system jezdny
uczenie głębokie
uczenie przez naśladowanie
Opis:
Reliability, efficiency and generalization are basic evaluation criteria for a vehicle automated driving system. This paper proposes an automated driving decision-making method based on the Wasserstein generative adversarial imitation learning–deep deterministic policy gradient (WGAIL–DDPG(λ)). Here the exact reward function is designed based on the requirements of a vehicle’s driving performance, i.e., safety, dynamic and ride comfort performance. The model’s training efficiency is improved through the proposed imitation learning strategy, and a gain regulator is designed to smooth the transition from imitation to reinforcement phases. Test results show that the proposed decision-making model can generate actions quickly and accurately according to the surrounding environment. Meanwhile, the imitation learning strategy based on expert experience and the gain regulator can effectively improve the training efficiency for the reinforcement learning model. Additionally, an extended test also proves its good adaptability for different driving conditions.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 461--470
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning (pogłębianie procesu uczenia się) z perspektywy analizy potrzeb studentów języka angielskiego jako obcego
Deep Learning from the Perspective of Needs Analysis of Students of English as a Foreign Language
Autorzy:
Papaja, Katarzyna
Świątek, Artur
Mielnik, Kamil
Powiązania:
https://bibliotekanauki.pl/articles/1398073.pdf
Data publikacji:
2019-12-31
Wydawca:
Ateneum - Akademia Nauk Stosowanych w Gdańsku
Tematy:
Deep Learning
process
teacher
student
foreign language
personalisation
transformation
deep learning
pogłębione uczenie się
proces
nauczyciel
uczeń
język obcy
personalizacja
transformacja
Opis:
Although the term Deep Learning does not seem to be a new term in language learning, it attracted relatively little attention until just a few years ago. Different fields of study show that Deep Learning leverages a sophisticated process to learn multiple levels of abstraction from the data; however, in languages, the term has been widely accepted as the key concept in the transformation and personalisation of the learning process. In this paper, we take the definition of Deep Learning, and we corroborate the theories by use of the study which aims to assess the needs of students in the context of language exercises, resources as well as tools and modern technological solutions. A proper understanding of Deep Learning is necessary to examine the potential benefits for students and the broadly defined society. Therefore, the essence of the research is to obtain the answers to what is important in the education of modern foreign languages and also what the teacher’s role is. A quantitative study was conducted on 441 students of English Philology. The results of the needs analysis of foreign language students allow for a greater understanding of their expectations towards themselves and their teachers; additionally, to answer the question about what kind of education recipients they are and whether they are active participants in the whole educational process.
Choć termin pogłębionego procesu uczenia się (deep learning) nie wydaje się być terminem nowym w nauczaniu języków, do niedawna przyciągnął stosunkowo niewiele uwagi naukowców. W wielu językach jednak termin ten został powszechnie zaakceptowany jako kluczowa koncepcja transformacji i personalizacji procesu uczenia się. W niniejszym artykule prezentujemy definicję deep learning i potwierdzamy teorię poprzez badanie, którego celem jest ocena potrzeb uczniów w kontekście ćwiczeń językowych, zasobów, a także narzędzi i nowoczesnych rozwiązań technologicznych. Prawidłowe zrozumienie pogłębionego uczenia się jest konieczne, aby zbadać potencjalne korzyści wynikające z niego dla studentów i szeroko rozumianego społeczeństwa. Dlatego też istotą prowadzonych badań jest uzyskanie odpowiedzi na pytanie, co jest ważne w dydaktyce współczesnych języków obcych, a także jaka jest rola nauczyciela w tym zakresie. Wyniki analiz potrzeb uczniów języków obcych pozwalają uzyskać wiedzę na temat ich oczekiwań wobec siebie samych oraz wobec nauczycieli, a także odpowiedzieć na pytanie, jakiego rodzaju odbiorcami edukacji są młodzi uczący się i czy aktywnie partycypują w globalnym procesie kształcenia.
Źródło:
Forum Filologiczne Ateneum; 2019, 7, 1; 301-320
2353-2912
2719-8537
Pojawia się w:
Forum Filologiczne Ateneum
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Effectiveness of Unsupervised Training in Deep Learning Neural Networks
Autorzy:
Rusiecki, Andrzej
Kordos, Mirosław
Powiązania:
https://bibliotekanauki.pl/articles/1373690.pdf
Data publikacji:
2015
Wydawca:
Uniwersytet Jagielloński. Wydawnictwo Uniwersytetu Jagiellońskiego
Tematy:
neural networks
deep learning
restricted Boltzmann Machine
contrastive divergence
Opis:
Deep learning is a field of research attracting nowadays much attention, mainly because deep architectures help in obtaining outstanding results on many vision, speech and natural language processing – related tasks. To make deep learning effective, very often an unsupervised pretraining phase is applied. In this article, we present experimental study evaluating usefulness of such approach, testing on several benchmarks and different percentages of labeled data, how Contrastive Divergence (CD), one of the most popular pretraining methods, influences network generalization.
Źródło:
Schedae Informaticae; 2015, 24; 41-51
0860-0295
2083-8476
Pojawia się w:
Schedae Informaticae
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
THE DE BONO LAMS SEQUENCE SERIES: TEMPLATE DESIGNS AS KNOWLEDGE-MOBILISING STRATEGY FOR 21ST CENTURY HIGHER EDUCATION
Autorzy:
Dobozy, Eva
Powiązania:
https://bibliotekanauki.pl/articles/941236.pdf
Data publikacji:
2012
Wydawca:
Uniwersytet Marii Curie-Skłodowskiej w Lublinie. IATEFL Poland Computer Special Interest Group
Tematy:
de Bono LAMS sequence series
student-producers
deep learning
Opis:
In this paper, the five interlocking de Bono LAMS sequences are introduced as a new form of generic template designs. This transdisciplinary knowledge-mobilising strategy is based on Edward de Bono’s attention-directing ideas and thinking skills, commonly known as the CoRT tools. The development of the de Bono LAMS sequence series is an important milestone, signifying the current paradigmatic shift in higher education from a student-consumer paradigm to a student-producer paradigm. Surpassing surface and shallow knowledge stages requires the use of multidisciplinary and generic knowledge in new and unfamiliar situations. The LAMS templates as ‘knowledge-in-practice’ models assist disciplinary specialists generate learning designs that make apparent to students that knowledge is always partial, incomplete and coloured by epistemological beliefs and cultural practices.
Źródło:
Teaching English with Technology; 2012, 12, 2; 88-102
1642-1027
Pojawia się w:
Teaching English with Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of deep learning neural networks for the diagnosis of electrical damage to the induction motor using the axial flux
Autorzy:
Skowron, M.
Powiązania:
https://bibliotekanauki.pl/articles/201768.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
induction motor
axial flux
deep learning
convolutional neural networks
Opis:
In industrial drive systems, one of the widest group of machines are induction motors. During normal operation, these machines are exposed to various types of damages, resulting in high economic losses. Electrical circuits damages are more than half of all damages appearing in induction motors. In connection with the above, the task of early detection of machine defects becomes a priority in modern drive systems. The article presents the possibility of using deep neural networks to detect stator and rotor damages. The opportunity of detecting shorted turns and the broken rotor bars with the use of an axial flux signal is presented.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1031-1038
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine
Autorzy:
Gholami, Alireza
Asgari, Kaveh
Khoshdast, Hamid
Hassanzadeh, Ahmad
Powiązania:
https://bibliotekanauki.pl/articles/2146884.pdf
Data publikacji:
2022
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
hybrid geometallurgy
historical data
deep learning
copper ore
flotation
Opis:
This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production.
Źródło:
Physicochemical Problems of Mineral Processing; 2022, 58, 3; art. no. 147841
1643-1049
2084-4735
Pojawia się w:
Physicochemical Problems of Mineral Processing
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning in pharmacology: opportunities and threats
Autorzy:
Kocić, Ivan
Kocić, Milan
Rusiecka, Izabela
Kocić, Adam
Kocić, Eliza
Powiązania:
https://bibliotekanauki.pl/articles/25728738.pdf
Data publikacji:
2022-09-06
Wydawca:
Gdański Uniwersytet Medyczny
Tematy:
machine learning
pharmacology
deep learning
artificial intelligence
drug research and development
Opis:
Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.
Źródło:
European Journal of Translational and Clinical Medicine; 2022, 5, 2; 88-94
2657-3148
2657-3156
Pojawia się w:
European Journal of Translational and Clinical Medicine
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Pre-trained deep neural network using sparse autoencoders and scattering wavelet transform for musical genre recognition
Autorzy:
Kleć, M.
Korzinek, D.
Powiązania:
https://bibliotekanauki.pl/articles/952940.pdf
Data publikacji:
2015
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
Sparse Autoencoders
deep learning
genre recognition
Scattering Wavelet Transform
Opis:
Research described in this paper tries to combine the approach of Deep Neural Networks (DNN) with the novel audio features extracted using the Scatter- Ing Wavelet Transform (SWT) for classifying musical genres. The SWT uses A sequence of Wavelet Transforms to compute the modulation spectrum coef- Ficients of multiple orders, which has already shown to be promising for this Task. The DNN in this work uses pre-trained layers using Sparse Autoencoders (SAE). Data obtained from the Creative Commons website jamendo.com is Used to boost the well-known GTZAN database, which is a standard bench- mark for this task. The final classifier is tested using a 10-fold cross validation To achieve results similar to other state-of-the-art approaches.
Źródło:
Computer Science; 2015, 16 (2); 133-144
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Survey on multi-objective based parameter optimization for deep learning
Autorzy:
Chakraborty, Mrittika
Pal, Wreetbhas
Bandyopadhyay, Sanghamitra
Maulik, Ujjwal
Powiązania:
https://bibliotekanauki.pl/articles/27312917.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
multi-objective optimization
parameter optimization
neural networks
Opis:
Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in all cases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the two methods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.
Źródło:
Computer Science; 2023, 24 (3); 327--359
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A progressive and cross-domain deep transfer learning framework for wrist fracture detection
Autorzy:
Karam, Christophe
El Zini, Julia
Awad, Mariette
Saade, Charbel
Naffaa, Lena
El Amine, Mohammad
Powiązania:
https://bibliotekanauki.pl/articles/2147130.pdf
Data publikacji:
2022
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
deep learning
transfer learning
wrist fracture detection
medical informatics
progressive transfer learning
Opis:
There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require expertise which is both expensive and time-consuming, especially in the medical field. Furthermore, in health care systems where mistakes can have catastrophic consequences, there is a general mistrust in the black-box aspect of AI models. In this work, we focus on improving the performance of medical imaging applications when limited data is available while focusing on the interpretability aspect of the proposed AI model. This is achieved by employing a novel transfer learning framework, progressive transfer learning, an automated annotation technique and a correlation analysis experiment on the learned representations. Progressive transfer learning helps jump-start the training of deep neural networks while improving the performance by gradually transferring knowledge from two source tasks into the target task. It is empirically tested on the wrist fracture detection application by first training a general radiology network RadiNet and using its weights to initialize RadiNetwrist, that is trained on wrist images to detect fractures. Experiments show that RadiNetwrist achieves an accuracy of 87% and an AUC ROC of 94% as opposed to 83% and 92% when it is pre-trained on the ImageNet dataset. This improvement in performance is investigated within an explainable AI framework. More concretely, the learned deep representations of RadiNetwrist are compared to those learned by the baseline model by conducting a correlation analysis experiment. The results show that, when transfer learning is gradually applied, some features are learned earlier in the network. Moreover, the deep layers in the progressive transfer learning framework are shown to encode features that are not encountered when traditional transfer learning techniques are applied. In addition to the empirical results, a clinical study is conducted and the performance of RadiNetwrist is compared to that of an expert radiologist. We found that RadiNetwrist exhibited similar performance to that of radiologists with more than 20 years of experience. This motivates follow-up research to train on more data to feasibly surpass radiologists’ performance, and investigate the interpretability of AI models in the healthcare domain where the decision-making process needs to be credible and transparent.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 101--120
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Yet another research on GANs in cybersecurity
Autorzy:
Zimoń, Michał
Kasprzyk, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/13946602.pdf
Data publikacji:
2023-02-20
Wydawca:
Akademia Sztuki Wojennej
Tematy:
cybersecurity
malware
artificial intelligence
machine learning
deep learning
generative adversarial networks
Opis:
Deep learning algorithms have achieved remarkable results in a wide range of tasks, including image classification, language translation, speech recognition, and cybersecurity. These algorithms can learn complex patterns and relationships from large amounts of data, making them highly effective for many applications. However, it is important to recognize that models built using deep learning are not fool proof and can be fooled by carefully crafted input samples. This paper presents the results of a study to explore the use of Generative Adversarial Networks (GANs) in cyber security. The results obtained confirm that GANs enable the generation of synthetic malware samples that can be used to mislead a classification model.
Źródło:
Cybersecurity and Law; 2023, 9, 1; 61-72
2658-1493
Pojawia się w:
Cybersecurity and Law
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep features extraction for robust fingerprint spoofing attack detection
Autorzy:
Souza de, Gustavo Botelho
Silva Santos da, Daniel Felipe
Gonçalves Pires, Rafael
Nilceu Marana, Aparecido
Paulo Papa, Joao
Powiązania:
https://bibliotekanauki.pl/articles/91725.pdf
Data publikacji:
2019
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
restricted Boltzmann Machines
Deep Boltzmann Machines
deep learning
fingerprint spoofing detection
biometrics
Opis:
Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding attack detection
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 41-49
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Power-Ground Plane Impedance Modeling Using Deep Neural Networks and an Adaptive Sampling Process
Autorzy:
Goay, Chan Hong
Cheong, Zheng Quan
Low, Chen En
Ahmad, Nur Syazreen
Goh, Patrick
Powiązania:
https://bibliotekanauki.pl/articles/2200709.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
adaptive sampling
deep neural networks
deep learning
power-ground plane
Z-parameters
Opis:
This paper proposes a deep neural network (DNN) based method for the purpose of power-ground plane impedance modeling. A composite DNN model, which is a combination of two DNNs is used to predict the Z-parameters of power ground planes from their design parameters. The first DNN predicts the normalized Z-parameters whereas the second DNN predicts the original maximum and minimum values of the nonnormalized Z-parameters. This allows the method to retain a high accuracy when predicting responses that have large variations across designs, as is the case with the Z-parameters of the power-ground planes. We use the adaptive sampling algorithm to generate the training and validation samples for the DNNs. The adaptive sampling algorithm starts with only a few samples, then slowly generates more samples in the non-linear regions within the design parameters space. The level of non-linearity of the regions is determined by a surrogate model which is also trained using the generated samples as well. If the surrogate model has poor prediction accuracy in a region, then the adaptive sampling algorithm will generate more samples in that region. A shallow neural network is used as the surrogate model for non-linearity determination of the regions since it is faster to train and update. Once all the samples have been generated, they will be used to train and validate the composite DNN models. Finally, we present two examples, a square-shaped power ground plane and a squareshaped power ground plane with a hollow square at the center to demonstrate the robustness of the DNN composite models.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 793--798
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:
Deep learning-based framework for tumour detection and semantic segmentation
Autorzy:
Kot, Estera
Krawczyk, Zuzanna
Siwek, Krzysztof
Królicki, Leszek
Czwarnowski, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/2128156.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
deep learning
medical imaging
tumour detection
semantic segmentation
image fusion
technika deep learning
głęboka nauka
obrazowanie medyczne
wykrywanie guza
segmentacja semantyczna
połączenie obrazu
Opis:
For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bearing fault detection and diagnosis based on densely connected convolutional networks
Autorzy:
Niyongabo, Julius
Zhang, Yingjie
Ndikumagenge, Jérémie
Powiązania:
https://bibliotekanauki.pl/articles/2105995.pdf
Data publikacji:
2022
Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Tematy:
bearing
deep learning
machine learning
transfer learning
fault detection
fault diagnosis
CWRU dataset
Opis:
Rotating machines are widely used in today’s world. As these machines perform the biggest tasks in industries, faults are naturally observed on their components. For most rotating machines such as wind turbine, bearing is one of critical components. To reduce failure rate and increase working life of rotating machinery it is important to detect and diagnose early faults in this most vulner-able part. In the recent past, technologies based on computational intelligence, including machine learning (ML) and deep learning (DL), have been efficiently used for detection and diagnosis of bearing faults. However, DL algorithms are being increasingly favoured day by day because of their advantages of automatically extracting features from training data. Despite this, in DL, adding neural layers reduces the training accuracy and the vanishing gradient problem arises. DL algorithms based on convolutional neural networks (CNN) such as DenseNet have proved to be quite efficient in solving this kind of problem. In this paper, a transfer learning consisting of fine-tuning DenseNet-121 top layers is proposed to make this classifier more robust and efficient. Then, a new intelligent model inspired by DenseNet-121 is designed and used for detecting and diagnosing bearing faults. Continuous wavelet transform is applied to enhance the dataset. Experimental results obtained from analyses employing the Case Western Reserve University (CWRU) bearing dataset show that the proposed model has higher diagnostic performance, with 98% average accuracy and less complexity.
Źródło:
Acta Mechanica et Automatica; 2022, 16, 2; 130--135
1898-4088
2300-5319
Pojawia się w:
Acta Mechanica et Automatica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A survey of big data classification strategies
Autorzy:
Banchhor, Chitrakant
Srinivasu, N.
Powiązania:
https://bibliotekanauki.pl/articles/2050171.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
big data
data mining
MapReduce
classification
machine learning
evolutionary intelligence
deep learning
Opis:
Big data plays nowadays a major role in finance, industry, medicine, and various other fields. In this survey, 50 research papers are reviewed regarding different big data classification techniques presented and/or used in the respective studies. The classification techniques are categorized into machine learning, evolutionary intelligence, fuzzy-based approaches, deep learning and so on. The research gaps and the challenges of the big data classification, faced by the existing techniques are also listed and described, which should help the researchers in enhancing the effectiveness of their future works. The research papers are analyzed for different techniques with respect to software tools, datasets used, publication year, classification techniques, and the performance metrics. It can be concluded from the here presented survey that the most frequently used big data classification methods are based on the machine learning techniques and the apparently most commonly used dataset for big data classification is the UCI repository dataset. The most frequently used performance metrics are accuracy and execution time.
Źródło:
Control and Cybernetics; 2020, 49, 4; 447-469
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Camera-based PHM method in rotating machinery equipment micro-action scenarios
Autorzy:
Junfeng, An
Liu, Jiqiang
Zhen, Hao
Mengmeng, Lu
Powiązania:
https://bibliotekanauki.pl/articles/24200809.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
deep learning
condition monitoring
Rmcad
anomaly detection
defect early warning
Opis:
The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 10
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism
Autorzy:
Li, Xueyi
Su, Kaiyu
He, Qiushi
Wang, Xiangkai
Xie, Zhijie
Powiązania:
https://bibliotekanauki.pl/articles/24200832.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
fault diagnosis
Bi-LSTM
attention
highway
deep learning
Ball Bearing
Opis:
Deep groove ball bearings are widely used in rotary machinery. Accurate for bearing faults diagnosis is essential for equipment maintenance. For common depth learning methods, the feature extraction of inverse time domain signal direction and the attention to key features are usually ignored. Based on the long short term memory(LSTM) network, this study proposes an attention-based highway bidirectional long short term memory (AHBi-LSTM) network for fault diagnosis based on the raw vibration signal. By increasing the Attention mechanism and Highway, the ability of the network to extract features is increased. The bidirectional LSTM network simultaneously extracts the raw vibration signal in positive and inverse time-domains to better extract the fault features. Six deep groove ball bearings with different health conditions were used to validate the AHBi-LSTM method in an experiment. The results showed that the accuracy of the proposed method for bearing fault diagnosis was over 98%, which was 8.66% higher than that of the LSTM model. The AHBi-LSTM model is also better than other relevant models for bearing fault diagnosis.
Źródło:
Eksploatacja i Niezawodność; 2023, 25, 2; art. no. 162937
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Career track prediction using deep learning model based on discrete series of quantitative classification
Autorzy:
Hernandez, Rowell
Atienza, Robert
Powiązania:
https://bibliotekanauki.pl/articles/1956033.pdf
Data publikacji:
2021
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
track prediction
deep learning
education
przewidywanie torów
głębokie uczenie
edukacja
Opis:
In this paper, a career track recommender system was proposed using Deep Neural Network model. This study aims to assist guidance counselors in guiding their students in the selection of a suitable career track. It is because a lot of Junior High school students experienced track uncertainty and there are instances of shifting to another program after learning they are not suited for the chosen track or course in college. In dealing with the selection of the best student attributes that will help in the creation of the predictive model, the feature engineering technique is used to remove the irrelevant features that can affect the performance of the DNN model. The study covers 1500 students from the first to the third batch of the K-12 curriculum, and their grades from 11 subjects, sex, age, number of siblings, parent’s income, and academic strand were used as attributes to predict their academic strand in Senior High School. The efficiency and accuracy of the algorithm depend upon the correctness and quality of the collected student’s data. The result of the study shows that the DNN algorithm performs reasonably well in predicting the academic strand of students with a predic-tion accuracy of 83.11%. Also, the work of guidance counselors became more efficient in handling students’ concerns just by using the proposed system. It is concluded that the recommender system serves as a decision tool for counselors in guiding their stu-dents to determine which Senior High School track is suitable for students with the utilization of the DNN model.
Źródło:
Applied Computer Science; 2021, 17, 4; 55-74
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
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