Skocz do pozycji: 1.

Tytuł:

Noise quantization simulation analysis of optical convolutional networks

Autorzy:

Zhang, Ye
Zhang, Saining
Zhang, Danni
Su, Yanmei
Yi, Junkai
Wang, Pengfei
Wang, Ruiting
Luo, Guangzhen
Zhou, Xuliang
Pan, Jiaoqing

Powiązania:

https://bibliotekanauki.pl/articles/27310111.pdf

Data publikacji:

2023

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

optical neural network
convolutional neural network
noise
quantization

Opis:

Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.

Źródło:

Optica Applicata; 2023, 53, 3; 483--493
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 2.

Tytuł:

Grid Search of Convolutional Neural Network model in the case of load forecasting

Autorzy:

Tran, Thanh Ngoc

Powiązania:

https://bibliotekanauki.pl/articles/1841362.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

load forecasting
grid search
convolutional neural network

Opis:

The Convolutional Neural Network (CNN) model is one of the most effective models for load forecasting with hyperparameters which can be used not only to determine the CNN structure and but also to train the CNN model. This paper proposes a frame work for Grid Search hyperparameters of the CNN model. In a training process, the optimalmodels will specify conditions that satisfy requirement for minimum of accuracy scoresof Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). In the testing process, these optimal models will be used to evaluate the results along with all other ones. The results indicated that the optimal models have accuracy scores near the minimum values. Load demand data of Queensland (Australia) and Ho Chi Minh City (Vietnam) were utilized to verify the accuracy and reliability of the Grid Search framework.

Źródło:

Archives of Electrical Engineering; 2021, 70, 1; 25-36
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Application of visual classification algorithms for identification of underwater audio signals

Autorzy:

Gnyś, Piotr
Szczęsna, Gabriela
Domínguez-Brito, Antonio C.
Cabrera-Gámez, Jorge

Powiązania:

https://bibliotekanauki.pl/articles/23956852.pdf

Data publikacji:

2022

Wydawca:

Politechnika Gdańska

Tematy:

audio processing
audio classification
convolutional neural network

Opis:

An audio processing and classification pipeline is presented in this work. The main focus is on the classification of sounds in a marine acoustic environment, however, the presented approach can be applied to other audio data. Audio samples from heterogeneous sources automatically spliced, normalized and transformed into spectrogram based visual representation are tagged on the pipeline input. The said representation is then used to train a convolutional neural network that can identify the presented categories in future recordings.

Źródło:

TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2022, 26, 4
1428-6394

Pojawia się w:

TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

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

/5

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Skocz do pozycji: 5.

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

/5

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Skocz do pozycji: 6.

Tytuł:

A novel method for automatic detection of arrhythmias using the unsupervised convolutional neural network

Autorzy:

Zhang, Junming
Yao, Ruxian
Gao, Jinfeng
Li, Gangqiang
Wu, Haitao

Powiązania:

https://bibliotekanauki.pl/articles/23944827.pdf

Data publikacji:

2023

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

convolutional neural network
arrhythmia detection
unsupervised learning
ECG classification

Opis:

In recent years, various models based on convolutional neural networks (CNN) have been proposed to solve the cardiac arrhythmia detection problem and achieved saturated accuracy. However, these models are often viewed as “blackbox” and lack of interpretability, which hinders the understanding of cardiologists, and ultimately hinders the clinical use of intelligent terminals. At the same time, most of these approaches are supervised learning and require label data. It is a time-consuming and expensive process to obtain label data. Furthermore, in human visual cortex, the importance of lateral connection is same as feed-forward connection. Until now, CNN based on lateral connection have not been studied thus far. Consequently, in this paper, we combines CNNs, lateral connection and autoencoder (AE) to propose the building blocks of lateral connection convolutional autoencoder neural networks (LCAN) for cardiac arrhythmia detection, which learn representations in an unsupervised manner. Concretely, the LCAN contains a convolution layer, a lateral connection layer, an AE layer, and a pooling layer. The LCAN detects salient wave features through the lateral connection layer. The AE layer and competitive learning is used to update the filters of the convolution network—an unsupervised process that ensures similar weight distribution for all adjacent filters in each convolution layer and realizes the neurons’ semantic arrangement in the LCAN. To evaluate the performances of the proposed model, we have implemented the experiments on the well-known MIT–BIH Arrhythmia Database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results show that the LCAN is not only effective, but also a useful tool for arrhythmia detection.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 3; 181--196
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

Tytuł:

Optimization of convolutional neural networks using the fuzzy gravitational search algorithm

Autorzy:

Poma, Yutzil
Melin, Patricia
González, Claudia I.
Martínez, Gabriela E.

Powiązania:

https://bibliotekanauki.pl/articles/384794.pdf

Data publikacji:

2020

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

neural networks
convolutional neural network
fuzzy gravitational search algorithm
deep learning

Opis:

This paper presents an approach to optimize a Convolutional Neural Network using the Fuzzy Gravitational Search Algorithm. The optimized parameters are the number of images per block that are used in the training phase, the number of filters and the filter size of the convolutional layer. The reason for optimizing these parameters is because they have a great impact on performance of the Convolutional Neural Networks. The neural network model presented in this work can be applied for any image recognition or classification applications; nevertheless, in this paper, the experiments are performed in the ORL and Cropped Yale databases. The results are compared with other neural networks, such as modular and monolithic neural networks. In addition, the experiments were performed manually, and the results were obtained (when the neural network is not optimized), and comparison was made with the optimized results to validate the advantage of using the Fuzzy Gravitational Search Algorithm.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 109-120
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior

Autorzy:

Peng, Guangze
Chen, Wenjing

Powiązania:

https://bibliotekanauki.pl/articles/2086757.pdf

Data publikacji:

2022

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

fringe projection profilometry
phase calculation
convolutional neural network
denoiser prior

Opis:

The intensity of some pixels of the captured fringe will be saturated when fringe projection profilometry is used to measure objects with high reflectivity, which will significantly affect the reconstruction of the measured object. In this paper, we propose a fringe pattern inpainting method based on the convolutional neural network (CNN) denoiser prior guided by additional information from a fringe captured in short exposure time. First, a binary mask obtained by Otsu algorithm from the modulation information of the short exposure fringe is used to detect the high-saturation region in the normal exposure fringe. Then, the corrected gray-scales of the region of the short exposure fringe selected by the mask are inserted in the saturated region of the normal fringe to form an initial fringe for iteration. At last, fringe pattern inpainting is achieved by using a CNN denoiser prior. The correct phase can be reconstructed from the inpainted fringes. The computer simulation and experiments verify the effectiveness of the proposed method.

Źródło:

Optica Applicata; 2022, 52, 2; 179--193
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Deep learning-based CNC milling tool wear stage estimation with multi-signal analysis

Autorzy:

Karabacak, Yunus Emre

Powiązania:

https://bibliotekanauki.pl/articles/27312777.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

wear stage estimation
milling
convolutional neural network
time-frequency analysis

Opis:

CNC milling machines are frequently used in the manufacturing of mechanical parts in the industry. One of the most important components of milling machines is the cutting tool. Monitoring the cutting tool wear is important for the reliability, continuity, and quality of production. Monitoring the tool and detecting the stage of wear are difficult processes. In this work, the convolutional neural network (CNN), which is a deep learning method in which the features are extracted by an inner process, was performed to detect the wear stages of the milling tool. These stages that define the total lifespan of the tool are known as initial wear (IW), steady-state wear (SSW), and accelerated wear (AW). Short Time Fourier Transform (STFT) was applied to signals, and signal spectrograms were used to train CNN models with different complex architectures. Vibration signals, acoustic emission signals, and motor current signals from The Nasa Ames Milling Dataset were used to obtain the spectrograms. Pre-trained CNNs (GoogleNet, AlexNet, ResNet-50, and EfficientNet-B0) detected the tool wear stage with varying accuracies. It has been seen that the time duration of model training increases as the size of the dataset grows and the network architecture becomes more complex. The recommended method has also been tested on the 2010 PHM Data Challenge Dataset. CNN shows promise for condition monitoring of milling operations and detecting tool wear stage.

Źródło:

Eksploatacja i Niezawodność; 2023, 25, 3; art. no. 168082
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

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

/5

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Skocz do pozycji: 11.

Tytuł:

Identifying selected diseases of leaves using deep learning and transfer learning models

Autorzy:

Mimi, Afsana
Zohura, Sayeda Fatema Tuj
Ibrahim, Muhammad
Haque, Riddho Ridwanul
Farrok, Omar
Jabid, Taskeed
Ali, Md Sawkat

Powiązania:

https://bibliotekanauki.pl/articles/2204260.pdf

Data publikacji:

2023

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej

Tematy:

convolutional neural network
transfer learning
leaf disease detection
image classification

Opis:

Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria ×ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time.

Źródło:

Machine Graphics & Vision; 2023, 32, 1; 55--71
1230-0535
2720-250X

Pojawia się w:

Machine Graphics & Vision

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

FPGA Implementation of Neural Nets

Autorzy:

Kumari, B A Sujatha
Kulkarni, Sudarshan Patil
Sinchana, C. G.

Powiązania:

https://bibliotekanauki.pl/articles/27311922.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

artificial neural network
Spartan-6
field programmable gate arrays (FPGAs)
convolutional neural network

Opis:

The field programmable gate array (FPGA) is used to build an artificial neural network in hardware. Architecture for a digital system is devised to execute a feed-forward multilayer neural network. ANN and CNN are very commonly used architectures. Verilog is utilized to describe the designed architecture. For the computation of certain tasks, a neural network’s distributed architecture structure makes it potentially efficient. The same features make neural nets suitable for application in VLSI technology. For the hardware of a neural network, a single neuron must be effectively implemented (NN). Reprogrammable computer systems based on FPGAs are useful for hardware implementations of neural networks.

Źródło:

International Journal of Electronics and Telecommunications; 2023, 69, 3; 599--604
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

Tytuł:

Deep networks for image super-resolution using hierarchical features

Autorzy:

Yang, Xin
Zhang, Yifan
Zhou, Dake

Powiązania:

https://bibliotekanauki.pl/articles/2173634.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

super-resolution
convolutional neural network
sub-pixel convolutional neural network
densely connected neural networks
super rozdzielczość
splotowa sieć neuronowa
subpikselowa splotowa sieć neuronowa
gęsto połączone sieci neuronowe

Opis:

To better extract feature maps from low-resolution (LR) images and recover high-frequency information in the high-resolution (HR) images in image super-resolution (SR), we propose in this paper a new SR algorithm based on a deep convolutional neural network (CNN). The network structure is composed of the feature extraction part and the reconstruction part. The extraction network extracts the feature maps of LR images and uses the sub-pixel convolutional neural network as the up-sampling operator. Skip connection, densely connected neural networks and feature map fusion are used to extract information from hierarchical feature maps at the end of the network, which can effectively reduce the dimension of the feature maps. In the reconstruction network, we add a 3×3 convolution layer based on the original sub-pixel convolution layer, which can allow the reconstruction network to have better nonlinear mapping ability. The experiments show that the algorithm results in a significant improvement in PSNR, SSIM, and human visual effects as compared with some state-of-the-art algorithms based on deep learning.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; art. no. e139616
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

Parallelization of Concise Convolutional Neural Networks for Plant Classification

Autorzy:

Sembiring, Arnes
Away, Yuwaldi
Arnia, Fitri
Muharar, Rusdha

Powiązania:

https://bibliotekanauki.pl/articles/2202377.pdf

Data publikacji:

2023

Wydawca:

Polskie Towarzystwo Inżynierii Ekologicznej

Tematy:

parallelisation
concise CNN
plant classification
multi-scale CNN
convolutional neural network

Opis:

Monitoring the agricultural field is the key to preventing the spread of disease and handling it quickly. The computer-based automatic monitoring system can meet the needs of large-scale and real-time monitoring. Plant classifiers that can work quickly in computer with limited resources are needed to realize this monitoring system. This study proposes convolutional neural network (CNN) architecture as a plant classifier based on leaf imagery. This architecture was built by parallelizing two concise CNN channels with different filter sizes using the addition operation. GoogleNet, SqueezeNet and MobileNetV2 were used to compare the performance of the proposed architecture. The classification performance of all these architectures was tested using the PlantVillage dataset which consists of 38 classes and 14 plant types. The experimental results indicated that the proposed architecture with a smaller number of parameters achieved nearly the same accuracy as the comparison architectures. In addition, the proposed architecture classified images 5.12 times faster than SqueezeNet, 8.23 times faster than GoogleNet, and 9.4 times faster than MobileNetV2. These findings suggest that when implemented in the agricultural field, the proposed architecture can be a reliable and faster plant classifier with fewer resources.

Źródło:

Journal of Ecological Engineering; 2023, 24, 2; 61--71
2299-8993

Pojawia się w:

Journal of Ecological Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

Tytuł:

A two-step fall detection algorithm combining threshold-based method and convolutional neural network

Autorzy:

Xu, Tao
Se, Haifeng
Liu, Jiahui

Powiązania:

https://bibliotekanauki.pl/articles/1848958.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

wearable
fall detection
MPU6050
threshold-based method
convolutional neural network

Opis:

Falls are one of the leading causes of disability and premature death among the elderly. Technical solutions designed to automatically detect a fall event may mitigate fall-related health consequences by immediate medical assistance. This paper presents a wearable device called TTXFD based on MPU6050 which can collect triaxial acceleration signals. We have also designed a two-step fall detection algorithm that fuses threshold-based method (TBM) and machine learning (ML). The TTXFD exploits the TBM stage with low computational complexity to pick out and transmit suspected fall data (triaxial acceleration data). The ML stage of the two-step algorithm is implemented on a server which encodes the data into an image and exploits a fall detection algorithm based on convolutional neural network to identify a fall on the basis of the image. The experimental results show that the proposed algorithm achieves high sensitivity (97.83%), specificity (96.64%) and accuracy (97.02%) on the open dataset. In conclusion, this paper proposes a reliable solution for fall detection, which combines the advantages of threshold-based method and machine learning technology to reduce power consumption and improve classification ability.

Źródło:

Metrology and Measurement Systems; 2021, 28, 1; 23-40
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Convolutional neural networks for P300 signal detection applied to brain computer interface

Autorzy:

Riyad, Mouad
Khalil, Mohammed
Adib, Abdellah

Powiązania:

https://bibliotekanauki.pl/articles/2141900.pdf

Data publikacji:

2020

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

deep learning
convolutional neural network
brain computer interface
P300
classification

Opis:

A Brain‐Computer Interface (BCI) is an instrument capa‐ ble of commanding machine with brain signal. The mul‐ tiple types of signals allow designing many applications like the Oddball Paradigms with P300 signal. We propose an EEG classification system applied to BCI using the con‐ volutional neural network (ConvNet) for P300 problem. The system consists of three stages. The first stage is a Spatiotemporal convolutional layer which is a succession of temporal and spatial convolutions. The second stage contains 5 standard convolutional layers. Finally, a lo‐ gistic regression is applied to classify the input EEG sig‐ nal. The model includes Batch Normalization, Dropout, and Pooling. Also, It uses Exponential Linear Unit (ELU) function and L1‐L2 regularization to improve the lear‐ ning. For experiments, we use the database Dataset II of the BCI Competition III. As a result, we get an F1‐score of 53.26% which is higher than the BN3 model.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 58-63
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

A DHCR_ SmartNet: A smart Devanagari handwritten character recognition using level-wised CNN architecture

Autorzy:

Deore, Shalaka Prasad

Powiązania:

https://bibliotekanauki.pl/articles/27312907.pdf

Data publikacji:

2022

Wydawca:

Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH

Tematy:

convolutional neural network
VGG16
fine-tuned
handwritten script
Devanagari characters

Opis:

Handwritten script recognition is a vital application of the machine-learning domain. Applications like automatic license plate detection, pin-code detection, and historical document management increases attention toward handwritten script recognition. English is the most widely spoken language in India; hence, there has been a lot of research into identifying a script using a machine. Devanagari is a popular script that is used by a large number of people on the Indian subcontinent. In this paper, a level-wised efficient transfer-learning approach is presented on the VGG16 model of a convolutional neural network (CNN) for identifying isolated Devanagari handwritten characters. In this work, a new dataset of Devanagari characters is presented and made accessible to the public. This newly created dataset is comprised of 5800 samples for 12 vowels, 36 consonants, and 10 digits. Initially, a simple CNN is implemented and trained on this new small dataset. During the next stage, a transfer-learning approach is implemented on the VGG16 model, and during the last stage, the efficient fine-tuned VGG16 model is implemented. The obtained accuracy of the fine-tuned model’s training and testing came to 98.16% and 96.47%, respectively.

Źródło:

Computer Science; 2022, 23 (3); 301--320
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

A distributed big data analytics model for traffic accidents classification and recognition based on SparkMlLib cores

Autorzy:

Mallahi, Imad El
Riffi, Jamal
Tairi, Hamid
Ez-Zahout, Abderrahmane
Mahraz, Mohamed Adnane

Powiązania:

https://bibliotekanauki.pl/articles/27314355.pdf

Data publikacji:

2022

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

big data
machine learning
traffic accident
severity prediction
convolutional neural network

Opis:

This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines provide a helpful and suitable API that helps to create and tune classification and prediction models to decision-making concerning traffic accidents. Data scientists have recently focused on classification and prediction techniques for traffic accidents; data analytics techniques for feature extraction have also continued to evolve. Analysis of a huge volume of received data requires considerable processing time. Practically, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in traffic accident recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from traffic accidents. Problems with overclocking during the digital processing of traffic accidents have yet to be completely resolved. Our proposed model is based on advanced processing by the Spark MlLib core. We call on the real-time data streaming API on spark to continuously gather real-time data from multiple external data sources in the form of data streams. Secondly, the data streams are treated as unbound tables. After this, we call the random forest algorithm continuously to extract the feature parameters from a traffic accident. The use of this proposed method makes it possible to increase the speed factor on processors. Experiment results showed that the proposed method successfully extracts the accident features and achieves a seamless classification performance compared to other conventional traffic accident recognition algorithms. Finally, we share all detected accidents with details onto online applications with other users.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 62--71
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

Plant disease detection using ensembled CNN framework

Autorzy:

Mondal, Subhash
Banerjee, Suharta
Mukherjee, Subinoy
Sengupta, Diganta

Powiązania:

https://bibliotekanauki.pl/articles/27312905.pdf

Data publikacji:

2022

Wydawca:

Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH

Tematy:

convolutional neural network
disease detection
ResNet-50
VGG-19
InceptionV3

Opis:

Agriculture exhibits the prime driving force for the growth of agro-based economies globally. In agriculture, detecting and preventing crops from the attacks of pests is a primary concern in today’s world. The early detection of plant disease becomes necessary in order to avoid the degradation of the yield of crop production. In this paper, we propose an ensemble-based convolutional neural network (CNN) architecture that detects plant disease from the images of a plant’s leaves. The proposed architecture considers CNN architectures like VGG-19, ResNet-50, and InceptionV3 as its base models, and the prediction from these models is used as an input for our meta-model (Inception-ResNetV2). This approach helped us build a generalized model for disease detection with an accuracy of 97.9% under test conditions.

Źródło:

Computer Science; 2022, 23 (3); 321--333
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network

Autorzy:

Fuada, S.
Shiddieqy, H. A.
Adiono, T.

Powiązania:

https://bibliotekanauki.pl/articles/1844462.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

fault detection
fault classification
transmission lines
convolutional neural network
machine learning

Opis:

To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.

Źródło:

International Journal of Electronics and Telecommunications; 2020, 66, 4; 655-664
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

VMD and CNN-Based Classification Model for Infrasound Signal

Autorzy:

Lu, Quanbo
Li, Mei

Powiązania:

https://bibliotekanauki.pl/articles/31339812.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

infrasound signal
variational mode decomposition
convolutional neural network
Fast Fourier Transform

Opis:

Infrasound signal classification is vital in geological hazard monitoring systems. The traditional classification approach extracts the features and classifies the infrasound events. However, due to the manual feature extraction, its classification performance is not satisfactory. To deal with this problem, this paper presents a classification model based on variational mode decomposition (VMD) and convolutional neural network (CNN). Firstly, the infrasound signal is processed by VMD to eliminate the noise. Then fast Fourier transform (FFT) is applied to convert the reconstructed signal into a frequency domain image. Finally, a CNN model is established to automatically extract the features and classify the infrasound signals. The experimental results show that the classification accuracy of the proposed classification model is higher than the other model by nearly 5%. Therefore, the proposed approach has excellent robustness under noisy environments and huge potential in geophysical monitoring.

Źródło:

Archives of Acoustics; 2023, 48, 3; 403-412
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Feature map augmentation to improve scale invariance in convolutional neural networks

Autorzy:

Kumar, Dinesh
Sharma, Dharmendra

Powiązania:

https://bibliotekanauki.pl/articles/2201321.pdf

Data publikacji:

2023

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

convolutional neural network
feature map augmentation
global features
scale-invariant
vision system

Opis:

Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any convolution layer within a CNN. However, its maximum effectiveness is shown when placed at the output end of final convolution layer. We test RiT in the application of scale-invariance where we attempt to classify scaled images from benchmark datasets. Our results show promising improvements in the networks ability to be scale invariant whilst keeping the model computation cost low.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 1; 51--74
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

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

/5

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Skocz do pozycji: 24.

Tytuł:

Simultaneous monitoring of chromatic dispersion and optical signal to noise ratio in optical links using convolutional neural network and asynchronous delay-tap sampling

Autorzy:

Mrozek, Tomasz
Perlicki, Krzysztof
Jakubiak, Andrzej

Powiązania:

https://bibliotekanauki.pl/articles/1835803.pdf

Data publikacji:

2020

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

deep learning
convolutional neural network
chromatic dispersion
OSNR
asynchronous delay-tap sampling

Opis:

The article presents a method for image analysis using asynchronous delay-tap sampling (ADTS) technique and convolutional neural networks (CNNs), allowing simultaneous monitoring of many phenomena occurring in the physical layer of the optical network. The ADTS method makes it possible to visualize the course of the optical signal in the form of characteristics (so-called phase portraits), which change their shape under the influence of phenomena (including chromatic dispersion, amplified spontaneous emission noise and other). Using the VPI photonics software,a simulation model of the ADTS technique was built. After the simulation tests, 10000 images were obtained, which after proper preparation were subjected to further analysis using CNN algorithms. The main goal of the study was to train a CNN to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses on the analysis of images containing simultaneously the phenomena of chromatic dispersion and optical signal to noise ratio.

Źródło:

Optica Applicata; 2020, 50, 3; 331-341
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

An intelligent compound gear-bearing fault identification approach using Bessel kernel-based time-frequency distribution

Autorzy:

Andrews, Athisayam
Manisekar, Kondal

Powiązania:

https://bibliotekanauki.pl/articles/2203369.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

compound gear-bearing faults
Bessel transform
time-frequency distribution
convolutional neural network

Opis:

The most crucial transmission components utilized in rotating machinery are gears and bearings. In a gearbox, the bearings support the force acting on the gears. Compound Faults in both the gears and bearings may cause heavy vibration and lead to early failure of components. Despite their importance, these compound faults are rarely studied since the vibration signals of the compound fault system are strongly dominated by noise. This work proposes an intelligent approach to fault identification of a compound gear-bearing system using a novel Bessel kernel-based Time-Frequency Distribution (TFD) called the Bessel transform. The Time-frequency images extracted using the Bessel transform are used as an input to the Convolutional Neural Network (CNN), which classifies the faults. The effectiveness of the proposed approach is validated with a case study, and a testing efficiency of 94% is achieved. Further, the proposed method is compared with the other TFDs and found to be effective.

Źródło:

Metrology and Measurement Systems; 2023, 30, 1; 83--97
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

Segmentation of cancer masses on breast ultrasound images using modified U-net
Segmentacja mas nowotworowych na obrazach ultrasonografii piersi z użyciem zmodyfikowanego modelu U-net

Autorzy:

Khallassi, Ihssane
El Yousfi Alaoui, My Hachem
Jilbab, Abdelilah

Powiązania:

https://bibliotekanauki.pl/articles/27315434.pdf

Data publikacji:

2023

Wydawca:

Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej

Tematy:

convolutional neural network
segmentation
u-net
residual neural network
konwolucyjna sieć neuronowa
segmentacja
rezydualna sieć neuronowa

Opis:

Breast cancer causes a huge number of women’s deaths every year. The accurate localization of a breast lesion is a crucial stage. The segmentation of breast ultrasound images participates in the improvement of the process of detection of breast anomalies. An automatic approach of segmentation of breast ultrasound images is presented in this paper, the proposed model is a modified u-net called Attention Residual U-net, designed to help radiologists in their clinical examination to determine adequately the limitation of breast tumors. Attention Residual U-net is a combination of existing models (Convolutional Neural Network U-net, the Attention Gate Mechanism and the Residual Neural Network). Public breast ultrasound images dataset of Baheya hospital in Egypt is used in this work. Dice coefficient, Jaccard index and Accuracy are used to evaluate the performance of the proposed model on the test set. Attention residual u-net can significantly give a dice coefficient = 90%, Jaccard index = 76% and Accuracy = 90%. The proposed model is compared with two other breast segmentation methods on the same dataset. The results show that the modified U-net model was able to achieve accurate segmentation of breast lesions in breast ultrasound images.
Każdego roku rak piersi powoduje ogromną liczbę zgonów kobiet. Dokładna lokalizacja zmiany piersi jest kluczowym etapem. Segmentacja obrazów ultrasonograficznych piersi przyczynia się do poprawy procesu wykrywania nieprawidłowości piersi. W tym artykule przedstawiono automatyczne podejście do segmentacji obrazów ultrasonograficznych piersi, proponowany model to zmodyfikowany U-net, nazwany Attention Residual U-net, zaprojektowany w celu wspomagania radiologów podczas badania klinicznego, w celu odpowiedniego określenia zasięgu guzów piersiowych. Attention Residual U-net jest połączeniem istniejących modeli (konwolucyjną siecią neuronową U-net, Attention Gate Mechanism i Residual Neural Network). W tym badaniu wykorzystano publiczny zbiór danych obrazów ultrasonograficznych piersi szpitala Baheya w Egipcie. Do oceny wydajności zaproponowanego modelu na zbiorze testowym wykorzystano współczynnik Dice'a, indeks Jaccarda i dokładność. Attention Residual U-net może znacznie przyczynić się do uzyskania współczynnika Dice'a równego 90%, indeksu Jaccarda równego 76% i dokładności równiej 90%. Proponowany model został porównany z dwoma innymi metodami segmentacji piersi na tym samym zbiorze danych. Wyniki pokazują, że zmodyfikowany model U-net był w stanie osiągnąć dokładną segmentację zmian piersiowych na obrazach ultrasonograficznych piersi.

Źródło:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2023, 13, 3; 11--15
2083-0157
2391-6761

Pojawia się w:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

Project of autonomous workstation feeding fledging birds
Projekt autonomicznego robota do karmienia podlotów

Autorzy:

Dwornicki, Dawid

Powiązania:

https://bibliotekanauki.pl/articles/2014198.pdf

Data publikacji:

2020

Wydawca:

Uniwersytet Technologiczno-Humanistyczny im. Kazimierza Pułaskiego w Radomiu

Tematy:

fledging birds
convolutional neural network
computer vision
podloty
konwolucyjne sieci neuronowe
analiza obrazu

Opis:

The article describes project of autonomous workstation capable of feeding fledging birds. During the breeding season animal rescue centers are experiencing huge overload of patients and up to 20% of patients are birds. Despite small size they demand as much care as other animals – in case of fledging birds main need is frequent feeding which is impossible to cover by working staff. Designed workstation is meant to solve this problem and decrease mortality of sick or immature animals.
Artykuł opisuje projekt stanowiska służącego do automatycznego karmienia podlotów. W sezonie lęgowym ośrodki rehabilitacji dzikich zwierząt zmagają się ze zwiększoną liczbą pacjentów, z których nawet do 20% stanowią ptaki. Mimo małych rozmiarów wymagają tyle samo opieki co pozostałe zwierzęta – w przypadku podlotów głównym zadaniem jest regularne i częste karmienie co jest niemożliwe do zrealizowania przez ograniczony zespół. Zaprojektowany robot ma za zadanie rozwiązać ten problem, wspomóc pracowników i zmniejszyć śmiertelność młodych lub chorych ptaków.

Źródło:

Journal of Automation, Electronics and Electrical Engineering; 2020, 2, 1; 9-15
2658-2058
2719-2954

Pojawia się w:

Journal of Automation, Electronics and Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

Intelligent fault diagnosis of rolling bearings based on continuous wavelet transform-multiscale feature fusion and improved channel attention mechanism

Autorzy:

Zhang, Jiqiang
Kong, Xiangwei
Cheng, Liu
Qi, Haochen
Yu, Mingzhu

Powiązania:

https://bibliotekanauki.pl/articles/24200817.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

deep learning
continuous wavelet transform
improved channel attention mechanism
multi-conditions
convolutional neural network

Opis:

Accurate fault diagnosis is critical to operating rotating machinery safely and efficiently. Traditional fault information description methods rely on experts to extract statistical features, which inevitably leads to the problem of information loss. As a result, this paper proposes an intelligent fault diagnosis of rolling bearings based on a continuous wavelet transform(CWT)-multiscale feature fusion and an improved channel attention mechanism. Different from traditional CNNs, CWT can convert the 1-D signals into 2-D images, and extract the wavelet power spectrum, which is conducive to model recognition. In this case, the multiscale feature fusion was implemented by the parallel 2-D convolutional neural networks to accomplish deeper feature fusion. Meanwhile, the channel attention mechanism is improved by converting from compressed to extended ways in the excitation block to better obtain the evaluation score of the channel. The proposed model has been validated using two bearing datasets, and the results show that it has excellent accuracy compared to existing methods.

Źródło:

Eksploatacja i Niezawodność; 2023, 25, 1; art. no. 16
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Denseformer for single image deraining

Autorzy:

Wang, Tianming
Wang, Kaige
Li, Qing

Powiązania:

https://bibliotekanauki.pl/articles/24987759.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

artificial intelligence
convolutional neural network
image deraining
sztuczna inteligencja
sieć neuronowa konwolucyjna
obraz pojedynczy

Opis:

Image is one of the most important forms of information expression in multimedia. It is the key factor to determine the visual effect of multimedia software. As an image restoration task, image deraining can effectively restore the original information of the image, which is conducive to the downstream task. In recent years, with the development of deep learning technology, CNN and Transformer structures have shone brightly in computer vision. In this paper, we summarize the key to success of these structures in the past, and on this basis, we introduce the concept of a layer aggregation mechanism to describe how to reuse the information of the previous layer to better extract the features of the current layer. Based on this layer aggregation mechanism, we build the rain removal network called DenseformerNet. Our network strengthens feature promotion and encourages feature reuse, allowing better information and gradient flow. Through a large number of experiments, we prove that our model is efficient and effective, and expect to bring some illumination to the future rain removal network.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 4; 651--661
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo

Autorzy:

Michael, Steve Olsen
Juan, Sarah Samson
Mit, Edwin

Powiązania:

https://bibliotekanauki.pl/articles/2058507.pdf

Data publikacji:

2022

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

acoustic modeling
automatic speech recognition
convolutional neural network
CNN
under-resourced language
NVIDIA NeMo

Opis:

For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models. There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). A CNN model is widely used as a method for improving image processing performance. In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language. Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data. Conversely, results and studies concerned with under-resourced languages remain unsatisfactory. Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper. Two experiments were conducted: the number of resources used in the works of our ASR’s training was manipulated, as was the internal parameter of the engine used, namely the epochs. The results of those experiments are then analyzed and compared with the results shown in existing papers.

Źródło:

Journal of Telecommunications and Information Technology; 2022, 1; 43--53
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

SpeakerNet for Cross-lingual Text-Independent Speaker Verification

Autorzy:

Habib, Hafsa
Tauseef, Huma
Fahiem, Muhammad Abuzar
Farhan, Saima
Usman, Ghousia

Powiązania:

https://bibliotekanauki.pl/articles/1953543.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

convolutional neural network
deep learning
Siamese network
speaker verification
text-independent
binary operation
Urdu speaker recognition

Opis:

Biometrics provide an alternative to passwords and pins for authentication. The emergence of machine learning algorithms provides an easy and economical solution to authentication problems. The phases of speaker verification protocol are training, enrollment of speakers and evaluation of unknown voice. In this paper, we addressed text independent speaker verification using Siamese convolutional network. Siamese networks are twin networks with shared weights. Feature space can be learnt easily by training these networks even if similar observations are placed in proximity. Extracted features from Siamese then can be classified using difference or correlation measures. We have implemented a customized scoring scheme that utilizes Siamese’ capability of applying distance measures with the convolutional learning. Experiments made on cross language audios of multi-lingual speakers confirm the capability of our architecture to handle gender, age and language independent speaker verification. Moreover, our designed Siamese network, SpeakerNet, provided better results than the existing speaker verification approaches by decreasing the equal error rate to 0.02.

Źródło:

Archives of Acoustics; 2020, 45, 4; 573-583
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Theory I: Deep networks and the curse of dimensionality

Autorzy:

Poggio, T.
Liao, Q.

Powiązania:

https://bibliotekanauki.pl/articles/200623.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

deep network
shallow network
convolutional neural network
function approximation
deep learning
sieci neuronowe
aproksymacja funkcji
uczenie głębokie

Opis:

We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 6; 761-773
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

Denoising and Analysis Methods of Computer Tomography Results of Lung Diagnostics for Use in Neural Network Technology

Autorzy:

Slavova, Oleksandra
Lebid, Solomiya

Powiązania:

https://bibliotekanauki.pl/articles/1833888.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Oddział w Lublinie PAN

Tematy:

computed tomography
CT scans analysis
convolutional neural network
image clustering
image denoising
k-means clustering

Opis:

Any type of biomedical screening emerges large amounts of data. As a rule, these data are unprocessed and might cause problems during the analysis and interpretation. It can be explained with inaccuracies and artifacts, which distort all the data. That is why it is crucial to make sure that the biomedical information under analysis was of high quality to omit to receive possibly wrong results or incorrect diagnosis. Receiving qualitative and trustworthy biomedical data is a necessary condition for high-quality data assessment and diagnostics. Neural networks as a computing system in data analysis provide recognizable and clear datasets. Without such data, it becomes extremely difficult to make a diagnosis, predict the course of the disease, and treatment result. The object of this research was to define, describe, and test a new approach to the analysis and preprocessing of the biomedical images, based on segmentation. Also, it was summarized different metrics for assessing image quality depending on the purpose of research. Based on the collected data, the advantages and disadvantages of each of the methods were identified. The proposed method of analysis and noise reduction was applied to the results of computed tomography lungs screening. Based on the appropriate evaluation metrics, the obtained results were evaluated quantitatively and qualitatively. As a result, the expediency of the proposed algorithm application was proven.

Źródło:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2020, 9, 1; 19--24
2084-5715

Pojawia się w:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 34.

Tytuł:

Blender jako narzędzie do generacji danych syntetycznych
Blender as a tool for generating synthetic data

Autorzy:

Sieczka, Rafał
Pańczyk, Maciej

Powiązania:

https://bibliotekanauki.pl/articles/98204.pdf

Data publikacji:

2020

Wydawca:

Politechnika Lubelska. Instytut Informatyki

Tematy:

artificial neural networks
convolutional neural network
synthetic data
blender
sztuczne sieci neuronowe
konwolucyjne sieci neuronowe
dane syntetyczne

Opis:

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data is difficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generate synthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained on a set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the results showed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of the data acquisition process and consequently, the learning process of neural networks.
Pozyskiwanie danych do treningu sieci neuronowych, jest kosztownym i pracochłonnym zadaniem, szczególnie kiedy takie dane są trudno dostępne. W niniejszym artykule zostało zaproponowane użycie programu do grafiki 3D Blender, jako narzędzia do automatycznej generacji danych syntetycznych zdjęć, na przykładzie etykiet cenowych. Przy użyciu biblioteki fastai, zostały wytrenowane klasyfikatory etykiet cenowych, na zbiorze danych syntetycznych, które porównano z klasyfikatorami trenowanymi na zbiorze danych rzeczywistych. Porównanie wyników wykazało, że możliwe jest użycie programu Blender do generacji danych syntetycznych. Pozwala to w znaczącym stopniu przyśpieszyć proces pozyskiwania danych, a co za tym idzie proces uczenia sieci neuronowych.

Źródło:

Journal of Computer Sciences Institute; 2020, 16; 227-232
2544-0764

Pojawia się w:

Journal of Computer Sciences Institute

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

A single upper limb pose estimation method based on the improved stacked hourglass network

Autorzy:

Peng, Gang
Zheng, Yuezhi
Li, Jianfeng
Yang, Jin

Powiązania:

https://bibliotekanauki.pl/articles/1838179.pdf

Data publikacji:

2021

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

convolutional neural network
stacked hourglass network
skeleton key point
single upper limb pose estimation
human machine coordination

Opis:

At present, most high-accuracy single-person pose estimation methods have high computational complexity and insufficient real-time performance due to the complex structure of the network model. However, a single-person pose estimation method with high real-time performance also needs to improve its accuracy due to the simple structure of the network model. It is currently difficult to achieve both high accuracy and real-time performance in single-person pose estimation. For use in human–machine cooperative operations, this paper proposes a single-person upper limb pose estimation method based on an end-to-end approach for accurate and real-time limb pose estimation. Using the stacked hourglass network model, a single-person upper limb skeleton key point detection model is designed. A deconvolution layer is employed to replace the up-sampling operation of the hourglass module in the original model, solving the problem of rough feature maps. Integral regression is used to calculate the position coordinates of key points of the skeleton, reducing quantization errors and calculations. Experiments show that the developed single-person upper limb skeleton key point detection model achieves high accuracy and that the pose estimation method based on the end-to-end approach provides high accuracy and real-time performance.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2021, 31, 1; 123-133
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Remanufacturing process improvement by image recognition methods. Application of the mechanical part
Poprawa procesu regeneracji poprzez metody rozpoznawania obrazu. Zastosowanie części mechanicznej

Autorzy:

Kos, Mateusz

Powiązania:

https://bibliotekanauki.pl/articles/1818701.pdf

Data publikacji:

2021

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

remanufacturing
convolutional neural network
automation
improvement
image recognition
regeneracja
konwolucyjne sieci neuronowe
automatyzacja
ulepszanie
rozpoznawanie obrazu

Opis:

The paper describes the possibility of using, building, and implementing an image recognition system in a company performing remanufacturing processes. It is based on a thesis prepared with the help of Wabco Reman Solutions. The tests were conducted using one of the parts remanufactured by the company – a manifold. The research focuses on different variants of the obtained image recognition models in order to identify differences that may affect their effectiveness and possible application in real work conditions. The environment used to build the models is Jupyter Notebook, and convolutional neural networks were implemented.
W artykule opisano możliwości wykorzystania, budowy i wdrożenia systemu rozpoznawania obrazu w firmie realizującej proces regeneracji. Artykuł powstał na podstawie pracy magisterskiej przygotowanej przy pomocy Wabco Reman Solutions. Przeprowadzone testy zostały wykonane na jednej z regenerowanych części w firmie - kolektorze. W badaniach skupiono się na różnych wariantach powstałych modeli rozpoznawania obrazów w celu dostrzeżenia różnic, które mogą wpłynąć na ich skuteczność i możliwość zastosowania w rzeczywistych warunkach pracy. Do budowy modeli, w których zaimplementowano konwolucyjne sieci neuronowe, wykorzystano środowisko Jupyter Notebook.

Źródło:

Journal of TransLogistics; 2021, 7, 1; 147--162
2450-5870

Pojawia się w:

Journal of TransLogistics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

An AI & ML based detection & identification in remote imagery: state-of-the-art

Autorzy:

Hashmi, Hina
Dwivedi, Rakesh
Kumar, Anil

Powiązania:

https://bibliotekanauki.pl/articles/2141786.pdf

Data publikacji:

2021

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

convolutional neural network
remote sensed imagery
object detection
artificial intelligence
feature extraction
deep learning
machine learning

Opis:

Remotely sensed images and their allied areas of application have been the charm for a long time among researchers. Remote imagery has a vast area in which it is serving and achieving milestones. From the past, after the advent of AL, ML, and DL-based computing, remote imagery is related techniques for processing and analyzing are continuously growing and offering countless services like traffic surveillance, earth observation, land surveying, and other agricultural areas. As Artificial intelligence has become the charm of researchers, machine learning and deep learning have been proven as the most commonly used and highly effective techniques for object detection. AI & ML-based object segmentation & detection makes this area hot and fond to the researchers again with the opportunities of enhanced accuracy in the same. Several researchers have been proposed their works in the form of research papers to highlight the effectiveness of using remotely sensed imagery for commercial purposes. In this article, we have discussed the concept of remote imagery with some preprocessing techniques to extract hidden and fruitful information from them. Deep learning techniques applied by various researchers along with object detection, object recognition are also discussed here. This literature survey is also included a chronological review of work done related to detection and recognition using deep learning techniques.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2021, 15, 4; 3-17
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Choice of the p-norm for high level classification features pruning in modern convolutional neural networks with local sensitivity analysis

Autorzy:

Jeczmionek, Ernest
Kowalski, Piotr A.

Powiązania:

https://bibliotekanauki.pl/articles/24988509.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

convolutional neural network
pruning
sensitivity analysis
transfer learning
ImageNet
sieć neuronowa konwolucyjna
analiza wrażliwości
uczenie transferowe

Opis:

Transfer learning has surfaced as a compelling technique in machine learning, enabling the transfer of knowledge across networks. This study evaluates the efficacy of ImageNet pretrained state-of-the-art networks, including DenseNet, ResNet, and VGG, in implementing transfer learning for prepruned models on compact datasets, such as FashionMNIST, CIFAR10, and CIFAR100. The primary objective is to reduce the number of neurons while preserving high-level features. To this end, local sensitivity analysis is employed alongside p-norms and various reduction levels. This investigation discovers that VGG16, a network rich in parameters, displays resilience to high-level feature pruning. Conversely, the ResNet architectures reveal an interesting pattern of increased volatility. These observations assist in identifying an optimal combination of the norm and the reduction level for each network architecture, thus offering valuable directions for model-specific optimization. This study marks a significant advance in understanding and implementing effective pruning strategies across diverse network architectures, paving the way for future research and applications.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 4; 663--672
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

Identification of advanced optical modulation format and estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

Autorzy:

Dong, Xiaowei
Yu, Zhihui

Powiązania:

https://bibliotekanauki.pl/articles/27310103.pdf

Data publikacji:

2023

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

deep learning
PT-CNN
parallel-twin convolutional neural network
constellation diagram
modulation format identification
SNR estimation

Opis:

In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.

Źródło:

Optica Applicata; 2023, 53, 2; 281--289
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

Tytuł:

Remanufacturing process improvement by image recognition methods. Application of the mechanical part
Poprawa procesu regeneracji poprzez metody rozpoznawania obrazu. Zastosowanie części mechanicznej

Autorzy:

Kos, Mateusz

Powiązania:

https://bibliotekanauki.pl/articles/1818687.pdf

Data publikacji:

2021

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

remanufacturing
convolutional neural network
automation
improvement
image recognition
regeneracja
konwolucyjne sieci neuronowe
automatyzacja
ulepszanie
rozpoznawanie obrazu

Opis:

The paper describes the possibility of using, building, and implementing an image recognition system in a company performing remanufacturing processes. It is based on a thesis prepared with the help of Wabco Reman Solutions. The tests were conducted using one of the parts remanufactured by the company – a manifold. The research focuses on different variants of the obtained image recognition models in order to identify differences that may affect their effectiveness and possible application in real work conditions. The environment used to build the models is Jupyter Notebook, and convolutional neural networks were implemented.
W artykule opisano możliwości wykorzystania, budowy i wdrożenia systemu rozpoznawania obrazu w firmie realizującej proces regeneracji. Artykuł powstał na podstawie pracy magisterskiej przygotowanej przy pomocy Wabco Reman Solutions. Przeprowadzone testy zostały wykonane na jednej z regenerowanych części w firmie - kolektorze. W badaniach skupiono się na różnych wariantach powstałych modeli rozpoznawania obrazów w celu dostrzeżenia różnic, które mogą wpłynąć na ich skuteczność i możliwość zastosowania w rzeczywistych warunkach pracy. Do budowy modeli, w których zaimplementowano konwolucyjne sieci neuronowe, wykorzystano środowisko Jupyter Notebook.

Źródło:

Journal of TransLogistics; 2021, 7, 1; 147--162
2450-5870

Pojawia się w:

Journal of TransLogistics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 41.

Tytuł:

A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

Autorzy:

Bernardo, Lucas Salvador
Damaševičius, Robertas
de Albuquerque, Victor Hugo C.
Maskeliūnas, Rytis

Powiązania:

https://bibliotekanauki.pl/articles/2055162.pdf

Data publikacji:

2021

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

Parkinson’s disease
spirography
convolutional neural network
deep learning
choroba Parkinsona
spirografia
sieć neuronowa konwolucyjna
uczenie głębokie

Opis:

Parkinson’s disease (PD) is the second most common neurological disorder in the world. Nowadays, it is estimated that it affects from 2% to 3% of the global population over 65 years old. In clinical environments, a spiral drawing task is performed to help to obtain the disease’s diagnosis. The spiral trajectory differs between people with PD and healthy ones. This paper aims to analyze differences between handmade drawings of PD patients and healthy subjects by applying the SqueezeNet convolutional neural network (CNN) model as a feature extractor, and a support vector machine (SVM) as a classifier. The dataset used for training and testing consists of 514 handwritten draws of Archimedes’ spiral images derived from heterogeneous sources (digital and paper-based), from which 296 correspond to PD patients and 218 to healthy subjects. To extract features using the proposed CNN, a model is trained and 20% of its data is used for testing. Feature extraction results in 512 features, which are used for SVM training and testing, while the performance is compared with that of other machine learning classifiers such as a Gaussian naive Bayes (GNB) classifier (82.61%) and a random forest (RF) (87.38%). The proposed method displays an accuracy of 91.26%, which represents an improvement when compared to pure CNN-based models such as SqueezeNet (85.29%), VGG11 (87.25%), and ResNet (89.22%).

Źródło:

International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 549--561
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 42.

Tytuł:

Zastosowanie sieci konwolucyjnej głębokiego uczenia w detekcji pojazdów
Use of deep learning convolutional network in vehicle detectionmears

Autorzy:

Oszutowska-Mazurek, D. A.
Mazurek, P.

Powiązania:

https://bibliotekanauki.pl/articles/136004.pdf

Data publikacji:

2017

Wydawca:

Wyższa Szkoła Techniczno-Ekonomiczna w Szczecinie

Tematy:

sieć konwolucyjna
uczenie głębokie
detekcja pojazdów
przetwarzanie obrazów
image processing
deep learning
convolutional neural network
vehicle detection

Opis:

Wstęp i cel: Detekcja pojazdów na znaczenie w bezpieczeństwie ruchu drogowego oraz programowaniu pojazdów autonomicznych. Celem pracy jest detekcja pojazdów odróżniająca obrazy pojazdów od innych obrazów nie zawierających pojazdów. Materiał i metody: W pracy wykorzystano bazę pojazdów zawierającą obrazy ekstrahowane z sekwencji wideo, które przetwarzano za pomocą sieci konwolucyjnej głębokiego uczenia. Wyniki: Uzyskana sieć konwolucyjna charakteryzuje się bardzo dobrymi parametrami, krzywa PSNR względem kroku uczenia rośnie co oznacza, że zachodzi proces odszumiania kerneli w całym procesie uczenia. Wniosek: Proponowana metoda może być wykorzystana w programowaniu pojazdów autonomicznych oraz implementacji w Inteligentnych Systemach Transportowych ITS do detekcji pojazdów; bazuje na uczeniu a nie na projektowaniu algorytmu syntetycznego, dzięki temu jest potrzebny relatywnie krótki czas opracowania klasyfikatora.
Introduction and aim: Vehicle detection plays essential role in road safety and automatic vehicle programming. The aim of study is vehicle detection distinguishing car and non-car images Material and methods: Vehicle database images extracted from video sequences were processed by deep learning convolutional network. Results: Obtained convolutional network is characterised by very good parameters, PSNR curve indicates denoising of kernels in learning process. Conclusion: Proposed method is potentially useful in autonomic vehicles programming and Intelligent Transportation Systems (ITS) for vehicles detection. The solution is based on learning, not on synthetic algorithm design, thanks to this, a relatively short time of classifier development is needed.

Źródło:

Problemy Nauk Stosowanych; 2017, 7; 47-56
2300-6110

Pojawia się w:

Problemy Nauk Stosowanych

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 43.

Tytuł:

The feature selection problem in computer-assisted cytology

Autorzy:

Kowal, M.
Skobel, M.
Nowicki, N.

Powiązania:

https://bibliotekanauki.pl/articles/329941.pdf

Data publikacji:

2018

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

nuclei segmentation
feature selection
breast cancer
convolutional neural network
segmentacja jądra
selekcja cech
rak piersi
sieć neuronowa konwolucyjna

Opis:

Modern cancer diagnostics is based heavily on cytological examinations. Unfortunately, visual inspection of cytological preparations under the microscope is a tedious and time-consuming process. Moreover, intra- and inter-observer variations in cytological diagnosis are substantial. Cytological diagnostics can be facilitated and objectified by using automatic image analysis and machine learning methods. Computerized systems usually preprocess cytological images, segment and detect nuclei, extract and select features, and finally classify the sample. In spite of the fact that a lot of different computerized methods and systems have already been proposed for cytology, they are still not routinely used because there is a need for improvement in their accuracy. This contribution focuses on computerized breast cancer classification. The task at hand is to classify cellular samples coming from fine-needle biopsy as either benign or malignant. For this purpose, we compare 5 methods of nuclei segmentation and detection, 4 methods of feature selection and 4 methods of classification. Nuclei detection and segmentation methods are compared with respect to recall and the F1 score based on the Jaccard index. Feature selection and classification methods are compared with respect to classification accuracy. Nevertheless, the main contribution of our study is to determine which features of nuclei indicate reliably the type of cancer. We also check whether the quality of nuclei segmentation/detection significantly affects the accuracy of cancer classification. It is verified using the test set that the average accuracy of cancer classification is around 76%. Spearman’s correlation and chi-square test allow us to determine significantly better features than the feature forward selection method.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 759-770
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

A few-shot fine-grained image recognition method

Autorzy:

Wang, Jianwei
Chen, Deyun

Powiązania:

https://bibliotekanauki.pl/articles/2204540.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

few-shot learning
attention metric
CNN
convolutional neural network
feature expression
wskaźnik uwagi
sieć neuronowa splotowa
cechy wyrażeń

Opis:

Deep learning methods benefit from data sets with comprehensive coverage (e.g., ImageNet, COCO, etc.), which can be regarded as a description of the distribution of real-world data. The models trained on these datasets are considered to be able to extract general features and migrate to a domain not seen in downstream. However, in the open scene, the labeled data of the target data set are often insufficient. The depth models trained under a small amount of sample data have poor generalization ability. The identification of new categories or categories with a very small amount of sample data is still a challenging task. This paper proposes a few-shot fine-grained image recognition method. Feature maps are extracted by a CNN module with an embedded attention network to emphasize the discriminative features. A channel-based feature expression is applied to the base class and novel class followed by an improved cosine similarity-based measurement method to get the similarity score to realize the classification. Experiments are performed on main few-shot benchmark datasets to verify the efficiency and generality of our model, such as Stanford Dogs, CUB-200, and so on. The experimental results show that our method has more advanced performance on fine-grained datasets.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 1; art. no. e144584
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

Restoration of Remote Satellite Sensing Images using Machine and Deep Learning : a Survey

Autorzy:

Abdellaoui, Meriem
Benabdelkader, Souad
Assas, Ouarda

Powiązania:

https://bibliotekanauki.pl/articles/31339413.pdf

Data publikacji:

2023

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej

Tematy:

image restoration
remote sensing images
artificial intelligence
AI
machine learning
ML
deep learning
DL
convolutional neural network
CNN

Opis:

Remote sensing satellite images are affected by different types of degradation, which poses an obstacle for remote sensing researchers to ensure a continuous and trouble-free observation of our space. This degradation can reduce the quality of information and its effect on the reliability of remote sensing research. To overcome this phenomenon, the methods of detecting and eliminating this degradation are used, which are the subject of our study. The original aim of this paper is that it proposes a state of art of recent decade (2012-2022) on advances in remote sensing image restoration using machine and deep learning, identified by this survey, including the databases used, the different categories of degradation, as well as the corresponding methods. Machine learning and deep learning based strategies for remote sensing satellite image restoration are recommended to achieve satisfactory improvements.

Źródło:

Machine Graphics & Vision; 2023, 32, 2; 147-167
1230-0535
2720-250X

Pojawia się w:

Machine Graphics & Vision

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Estimating the distance to an object from grayscale stereo images using deep learning

Autorzy:

Kulawik, Joanna

Powiązania:

https://bibliotekanauki.pl/articles/2202043.pdf

Data publikacji:

2022

Wydawca:

Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej

Tematy:

estimating distance
stereo vision
convolutional neural network
deep learning
szacowanie odległości
widzenie stereoskopowe
konwolucyjne sieci neuronowe
uczenie głębokie

Opis:

This article presents an innovative proposal for estimating the distance between an autonomous vehicle and an object in front of it. Such information can be used, for example, to support the process of controlling an autonomous vehicle. The primary source of information in research is monochrome stereo images. The images were made in compliance with the laws of the canonical order. The developed convolutional neural network model was used for the estimation. A proprietary dataset was developed for the experiments. The analysis was based on the phenomenon of disparity in stereo images. As a result of the research, a correctly trained model of the CNN network was obtained in six variants. High accuracy of distance estimation was achieved. This publication describes an original proposal for a hybrid blend of digital image analysis, stereo-vision, and deep learning for engineering applications.

Źródło:

Journal of Applied Mathematics and Computational Mechanics; 2022, 21, 4; 60--72
2299-9965

Pojawia się w:

Journal of Applied Mathematics and Computational Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

Tytuł:

A genetic algorithm based optimized convolutional neural network for face recognition

Autorzy:

Karlupia, Namrata
Mahajan, Palak
Abrol, Pawanesh
Lehana, Parveen K.

Powiązania:

https://bibliotekanauki.pl/articles/2201023.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

convolutional neural network
genetic algorithm
deep learning
evolutionary technique
sieć neuronowa konwolucyjna
algorytm genetyczny
uczenie głębokie
technika ewolucyjna

Opis:

Face recognition (FR) is one of the most active research areas in the field of computer vision. Convolutional neural networks (CNNs) have been extensively used in this field due to their good efficiency. Thus, it is important to find the best CNN parameters for its best performance. Hyperparameter optimization is one of the various techniques for increasing the performance of CNN models. Since manual tuning of hyperparameters is a tedious and time-consuming task, population based metaheuristic techniques can be used for the automatic hyperparameter optimization of CNNs. Automatic tuning of parameters reduces manual efforts and improves the efficiency of the CNN model. In the proposed work, genetic algorithm (GA) based hyperparameter optimization of CNNs is applied for face recognition. GAs are used for the optimization of various hyperparameters like filter size as well as the number of filters and of hidden layers. For analysis, a benchmark dataset for FR with ninety subjects is used. The experimental results indicate that the proposed GA-CNN model generates an improved model accuracy in comparison with existing CNN models. In each iteration, the GA minimizes the objective function by selecting the best combination set of CNN hyperparameters. An improved accuracy of 94.5% is obtained for FR.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 21--31
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 48.

Tytuł:

A Study on the Impact of Lombard Effect on Recognition of Hindi Syllabic Units Using CNN Based Multimodal ASR Systems

Autorzy:

Uma Maheswari, Sadasivam
Shahina, A.
Rishickesh, Ramesh
Nayeemulla Khan, A.

Powiązania:

https://bibliotekanauki.pl/articles/176415.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

Lombard speech
multimodal ASR
throat microphone
visual speech
Convolutional Neural Network
Hidden Markov Model
late fusion
intermediate fusion

Opis:

Research work on the design of robust multimodal speech recognition systems making use of acoustic, and visual cues, extracted using the relatively noise robust alternate speech sensors is gaining interest in recent times among the speech processing research fraternity. The primary objective of this work is to study the exclusive influence of Lombard effect on the automatic recognition of the confusable syllabic consonant-vowel units of Hindi language, as a step towards building robust multimodal ASR systems in adverse environments in the context of Indian languages which are syllabic in nature. The dataset for this work comprises the confusable 145 consonant-vowel (CV) syllabic units of Hindi language recorded simultaneously using three modalities that capture the acoustic and visual speech cues, namely normal acoustic microphone (NM), throat microphone (TM) and a camera that captures the associated lip movements. The Lombard effect is induced by feeding crowd noise into the speaker’s headphone while recording. Convolutional Neural Network (CNN) models are built to categorise the CV units based on their place of articulation (POA), manner of articulation (MOA), and vowels (under clean and Lombard conditions). For validation purpose, corresponding Hidden Markov Models (HMM) are also built and tested. Unimodal Automatic Speech Recognition (ASR) systems built using each of the three speech cues from Lombard speech show a loss in recognition of MOA and vowels while POA gets a boost in all the systems due to Lombard effect. Combining the three complimentary speech cues to build bimodal and trimodal ASR systems shows that the recognition loss due to Lombard effect for MOA and vowels reduces compared to the unimodal systems, while the POA recognition is still better due to Lombard effect. A bimodal system is proposed using only alternate acoustic and visual cues which gives a better discrimination of the place and manner of articulation than even standard ASR system. Among the multimodal ASR systems studied, the proposed trimodal system based on Lombard speech gives the best recognition accuracy of 98%, 95%, and 76% for the vowels, MOA and POA, respectively, with an average improvement of 36% over the unimodal ASR systems and 9% improvement over the bimodal ASR systems.

Źródło:

Archives of Acoustics; 2020, 45, 3; 419-431
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 49.

Tytuł:

Impact of low resolution on image recognition with deep neural networks: An experimental study

Autorzy:

Koziarski, M.
Cyganek, B.

Powiązania:

https://bibliotekanauki.pl/articles/330321.pdf

Data publikacji:

2018

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

image recognition
deep neural network
convolutional neural network
low resolution
super resolution
rozpoznawanie obrazu
sieć neuronowa głęboka
sieć neuronowa konwolucyjna
niska rozdzielczość
nadrozdzielczość

Opis:

Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 735-744
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 50.

Tytuł:

Plant classification based on leaf edges and leaf morphological veins using wavelet convolutional neural network

Autorzy:

Dewi, Wulan
Utomo, Wiranto Herry

Powiązania:

https://bibliotekanauki.pl/articles/1837797.pdf

Data publikacji:

2021

Wydawca:

Polskie Towarzystwo Promocji Wiedzy

Tematy:

classification
leaf edges
leaf veins morphological
wavelet convolutional neural network
klasyfikacja
brzegi liści
budowa morfologiczna liści
splotowa sieć neuronowa

Opis:

The leaf is one of the plant organs, contains chlorophyll, and functions as a catcher of energy from sunlight which is used for photosynthesis. Perfect leaves are composed of three parts, namely midrib, stalk, and leaf blade. The way to identify the type of plant is to look at the shape of the leaf edges. The shape, color, and texture of a plant's leaf margins may influence its leaf veins, which in this vein morphology carry information useful for plant classification when shape, color, and texture are not noticeable. Humans, on the other hand, may fail to recognize this feature because they prefer to see plants solely based on leaf form rather than leaf margins and veins. This research uses the Wavelet method to denoise existing images in the dataset and the Convolutional Neural Network classifies through images. The results obtained using the Wavelet Convolutional Neural Network method are equal to 97.13%.

Źródło:

Applied Computer Science; 2021, 17, 1; 81-89
1895-3735

Pojawia się w:

Applied Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 51.

Tytuł:

Convolutional Neural Networks as Context-Scraping Tools in Architecture and Urban Planning
Splotowe sieci neuronowe jako narzędzia służące wydobywaniu danych architektoniczno-urbanistycznych

Autorzy:

Dzieduszyński, Tomasz

Powiązania:

https://bibliotekanauki.pl/articles/2064144.pdf

Data publikacji:

2022

Wydawca:

PWB MEDIA Zdziebłowski

Tematy:

sieć neuronowa splotowa
architektura
urbanistyka
miasto inteligentne
wydobywanie danych
CAAD
convolutional neural network
architecture
urban planning
smart city
data scraping

Opis:

"Data scraping" is a term usually used in Web browsing to refer to the automated process of data extraction from websites or interfaces designed for human use. Currently, nearly two thirds of Net traffic are generated by bots rather than humans. Similarly, Deep Convolutional Neural Networks (CNNs) can be used as artificial agents scraping cities for relevant contexts. The convolutional filters, which distinguish CNNs from the Fully-connected Neural Networks (FNNs), make them very promising candidates for feature detection in the abundant and easily accessible smart-city data consisting of GIS and BIM models, as well as satellite imagery and sensory outputs. These new, convolutional city users could roam the abstract, digitized spaces of our cities to provide insight into the architectural and urban contexts relevant to design and management processes. This article presents the results of a query of the state-of-the-art applications of Convolutional Neural Networks as architectural “city scrapers” and proposes a new, experimental framework for utilization of CNNs in context scraping in urban scale.
„Data scraping” to termin używany zazwyczaj w kontekście ruchu sieciowego, oznaczający proces automatycznej ekstrakcji danych ze stron internetowych i interfejsów, zaprojektowanych do stosowania przez człowieka. Obecnie blisko dwie trzecie ruchu internetowego jest generowanych przez boty, a nie przez ludzi. Na podobnej zasadzie głębokie splotowe sieci neuronowe (CNN) mogą być stosowane jako narzędzia wyszukujące w miastach stosowne konteksty urbanistyczne. Filtry splotowe, odróżniające CNN od sieci w pełni połączonych (FNN), sprawiają, że są one obiecującymi kandydatami do wykrywania cech ukrytych w zasobnych i łatwo dostępnych danych smart city, składających się z modeli GIS i BiM oraz obrazów satelitarnych oraz innych danych sensorycznych. Filtry splotowe mogą przemierzać abstrakcyjne, cyfrowe przestrzenie naszych miast, dostarczając kontekstów przydatnych w projektowaniu oraz zarządzaniu architektoniczno-urbanistycznym. Artykuł prezentuje wyniki kwerendy źródeł dotyczących najnowszych zastosowań splotowych sieci neuronowych w wydobywaniu danych miejskich i proponuje nowe, eksperymentalne ramy dla wykorzystania CNN w ekstrakcji kontekstów urbanistycznych.

Źródło:

Builder; 2022, 26, 3; 79--81
1896-0642

Pojawia się w:

Builder

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 52.

Tytuł:

A weighted wrapper approach to feature selection

Autorzy:

Kusy, Maciej
Zajdel, Roman

Powiązania:

https://bibliotekanauki.pl/articles/2055180.pdf

Data publikacji:

2021

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

feature selection
wrapper approach
feature significance
weighted combined ranking
convolutional neural network
classification accuracy
selekcja cech
sieć neuronowa konwolucyjna
dokładność klasyfikacji

Opis:

This paper considers feature selection as a problem of an aggregation of three state-of-the-art filtration methods: Pearson’s linear correlation coefficient, the ReliefF algorithm and decision trees. A new wrapper method is proposed which, on the basis of a fusion of the above approaches and the performance of a classifier, is capable of creating a distinct, ordered subset of attributes that is optimal based on the criterion of the highest classification accuracy obtainable by a convolutional neural network. The introduced feature selection uses a weighted ranking criterion. In order to evaluate the effectiveness of the solution, the idea is compared with sequential feature selection methods that are widely known and used wrapper approaches. Additionally, to emphasize the need for dimensionality reduction, the results obtained on all attributes are shown. The verification of the outcomes is presented in the classification tasks of repository data sets that are characterized by a high dimensionality. The presented conclusions confirm that it is worth seeking new solutions that are able to provide a better classification result while reducing the number of input features.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 685--696
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 53.

Tytuł:

Arabic and American Sign Languages Alphabet Recognition by Convolutional Neural Network

Autorzy:

Alshomrani, Shroog
Aljoudi, Lina
Arif, Muhammad

Powiązania:

https://bibliotekanauki.pl/articles/2023675.pdf

Data publikacji:

2021

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

convolutional neural network
deep learning
American sign language
Arabic sign language
sieć neuronowa
głębokie uczenie
amerykański język migowy
arabski język migowy

Opis:

Hearing loss is a common disability that occurs in many people worldwide. Hearing loss can be mild to complete deafness. Sign language is used to communicate with the deaf community. Sign language comprises hand gestures and facial expressions. However, people find it challenging to communicate in sign language as not all know sign language. Every country has developed its sign language like spoken languages, and there is no standard syntax and grammatical structure. The main objective of this research is to facilitate the communication between deaf people and the community around them. Since sign language contains gestures for words, sentences, and letters, this research implemented a system to automatically recognize the gestures and signs using imaging devices like cameras. Two types of sign languages are considered, namely, American sign language and Arabic sign language. We have used the convolutional neural network (CNN) to classify the images into signs. Different settings of CNN are tried for Arabic and American sign datasets. CNN-2 consisting of two hidden layers produced the best results (accuracy of 96.4%) for the Arabic sign language dataset. CNN-3, composed of three hidden layers, achieved an accuracy of 99.6% for the American sign dataset.

Źródło:

Advances in Science and Technology. Research Journal; 2021, 15, 4; 136-148
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 54.

Tytuł:

Towards a new deep learning algorithm based on GRU and CNN: NGRU

Autorzy:

Atassi, Abdelhamid
el Azami, Ikram

Powiązania:

https://bibliotekanauki.pl/articles/2141895.pdf

Data publikacji:

2020

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

Convolutional Neural Network
CNN
Gated Recurrent Unit
GRU
SemEval
Twitter
word2vec
Keras
TensorFlow
Adadelta
Adam
soft-max
deep learning

Opis:

This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will compare with the second system which uses Gated Recurrent Unit (GRU). And through this comparison we propose a new system based on the positive points of the two previous systems. Therefore, this new system will take the right choice of hyper-parameters recommended by the authors of both systems. At the final stage we propose a method to apply this new system to the dataset of different languages (used especially in socials networks).

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 45-47
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 55.

Tytuł:

Investigation of the Lombard effect based on a machine learning approach

Autorzy:

Korvel, Gražina
Treigys, Povilas
Kąkol, Krzysztof
Kostek, Bożena

Powiązania:

https://bibliotekanauki.pl/articles/24200693.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

Lombard effect
speech detection
noise signal
self similarity matrix
convolutional neural network
efekt Lombarda
wykrywanie mowy
sygnał szumowy
sieć neuronowa konwolucyjna

Opis:

The Lombard effect is an involuntary increase in the speaker’s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method for detecting the Lombard effect in uttered speech. The influence of interfering noise, room type, and the gender of the person on the detection process is examined. First, acoustic parameters related to speech changes produced by the Lombard effect are extracted. Mid-term statistics are built upon the parameters and used for the self-similarity matrix construction. They constitute input data for a convolutional neural network (CNN). The self-similarity-based approach is then compared with two other methods, i.e., spectrograms used as input to the CNN and speech acoustic parameters combined with the k-nearest neighbors algorithm. The experimental investigations show the superiority of the self-similarity approach applied to Lombard effect detection over the other two methods utilized. Moreover, small standard deviation values for the self-similarity approach prove the resulting high accuracies.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 479--492
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 56.

Tytuł:

Combining Spectral Analysis with Artificial Intelligence in Heart Sound Study

Autorzy:

Kucharski, Dariusz
Kajor, Marcin
Grochala, Dominik
Iwaniec, Marek
Iwaniec, Joanna

Powiązania:

https://bibliotekanauki.pl/articles/102508.pdf

Data publikacji:

2019

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

deep learning
heart sound classification
convolutional neural network
machine learning
signal processing
uczenie głębokie
klasyfikacja dźwięku serca
splotowa sieć neuronowa
uczenie maszynowe
przetwarzanie sygnałów

Opis:

The auscultation technique has been widely used in medicine as a screening examination for ages. Nowadays, advanced electronics and effective computational methods aim to support the healthcare sector by providing dedicated solutions which help physicians and support diagnostic process. In this paper, we propose a machine learning approach for the analysis of heart sounds. We used the spectral analysis of acoustic signal to calculate feature vectors and tested a set of machine learning approaches to provide the most effective detection of cardiac disorders. Finally, we achieved 91% of sensitivity and 99% of positive predictivity for a designed algorithm based on convolutional neural network.

Źródło:

Advances in Science and Technology. Research Journal; 2019, 13, 2; 112-118
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 57.

Tytuł:

A deep hybrid model for human-computer interaction using dynamic hand gesture recognition

Autorzy:

Ramalingam, Brindha
Angappan, Geetha

Powiązania:

https://bibliotekanauki.pl/articles/38702766.pdf

Data publikacji:

2023

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

dynamic hand gesture
human-computer interaction
long short-term memory
convolutional neural network
dynamiczny gest ręki
interakcja człowiek-komputer
pamięć krótkotrwała
konwolucyjna sieć neuronowa

Opis:

Dynamic hand gestures attract great interest and are utilized in different fields. Amongthese, man-machine interaction is an interesting area that makes use of the hand to providea natural way of interaction between them. A dynamic hand gesture recognition system isproposed in this paper, which helps to perform control operations in applications such asmusic players, video games, etc. The key motivation of this research is to provide a simple, touch-free system for effortless and faster human-computer interaction (HCI). As thisproposed model employs dynamic hand gestures, HCI is achieved by building a modelwith a convolutional neural network (CNN) and long short-term memory (LSTM) net-works. CNN helps in extracting important features from the images and LSTM helpsto extract the motion information between the frames. Various models are constructedby differing the LSTM and CNN layers. The proposed system is tested on an existing EgoGesture dataset that has several classes of gestures from which the dynamic gesturesare utilized. This dataset is used as it has more data with a complex background, actionsperformed with varying speeds, lighting conditions, etc. This proposed hand gesture recognition system attained an accuracy of 93%, which is better than other existing systemssubject to certain limitations.

Źródło:

Computer Assisted Methods in Engineering and Science; 2023, 30, 3; 263-276
2299-3649

Pojawia się w:

Computer Assisted Methods in Engineering and Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 58.

Tytuł:

Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox
Diagnostyka błędów przekładni przemysłowych z wykorzystaniem zmodyfikowanej splotowej sieci neuronowej z globalnym uśrednieniem wartości dla poszczególnych kanałów

Autorzy:

Li, Yaxin
Wang, Kesheng

Powiązania:

https://bibliotekanauki.pl/articles/300870.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

modified convolutional neural network
global average pooling
intelligent fault diagnosis
industrial Gearbox
zmodyfikowana splotowa sieć neuronowa
globalne uśrednienie względem kanałów
inteligentna diagnostyka błędów
przekładnia przemysłowa

Opis:

Gearboxes are key transmission components and widely used in various industrial applications. Due to the possible operational conditions, such as varying rotational speeds, long period of heavy loads, etc., gearboxes may easily be prone to failure. Condition Monitoring (CM) has been proved to be an effective methodology to improve the safety and reliability of gearboxes. Deep learning approaches, nowadays, further enable the CM with more powerful capability to exploit faulty information from massive data and make intelligently diagnostic decisions. However, for most of conventional deep learning models, such as Convolutional Neural Network (CNN), a large amount of labelled training data is a prerequisite, while to obtain the labelled data is usually a laborious and time-consuming job and sometimes even unattainable. In this paper, to handle the case of only a limited labelled data is available, a modified convolutional neural network (MCNN) is proposed by integrating global average pooling (GAP) to reduce the number of trainable parameters and simplify the architecture of deep learning model. The proposed MCNN improves the traditional CNN’s ability in fault diagnosis with limited labelled data. Two experimental gearbox datasets are utilized to demonstrate the effectiveness of the proposed MCNN method. Compared with traditional deep learning approaches, namely LSTM, CNN and its variant methods, the experimental results show that the proposed MCNN with higher discrimination and generalization ability in fault classification and diagnostics under the scenario of limited labelled training samples.
Przekładnie stanowią kluczowe elementy układów napędowych i jako takie znajdują szerokie zastosowane w przemyśle. Ze względu na warunki eksploatacji, takie jak różne prędkości obrotowe czy długie okresy pracy pod dużym obciążeniem itp., przekładnie mogą łatwo ulegać uszkodzeniom. Udowodniono, że monitorowanie stanu skutecznie poprawia bezpieczeństwo i niezawodność przekładni. Podejścia oparte na uczeniu głębokim umożliwiają ponadto monitorowanie stanu z większym wykorzystaniem informacji o błędach pochodzących z dużych zbiorów danych i podejmowanie inteligentnych decyzji diagnostycznych. Jednak w przypadku większości konwencjonalnych modeli uczenia głębokiego, takich jak splotowe sieci neuronowe (convolutional neural networks, CNN), wymagana jest duża ilość etykietowanych danych uczących, których pozyskanie jest zwykle zadaniem praco- i czasochłonnym, a czasem wręcz niemożliwym do wykonania. W niniejszej pracy, przedstawiono zmodyfikowaną splotową sieć neuronową (modified convolutional neural network, MCNN), która rozwiązuje problem dostępności danych etykietowanych poprzez zastosowanie globalnego uśrednienia względem kanałów (global average pooling), co pozwala na zmniejszenie liczby możliwych do wyuczenia parametrów i uproszczenie architektury modelu głębokiego uczenia. W porównaniu do tradycyjnych sieci CNN, proponowana sieć MCNN zwiększa możliwości diagnozowania błędów przy ograniczonych danych etykietowanych. Skuteczność proponowanej metody wykazano na przykładzie dwóch zbiorów danych doświadczalnych dotyczących błędów przekładni. Wyniki eksperymentalne pokazują, że, w porównaniu z tradycyjnymi metodami uczenia głębokiego, takimi jak LSTM, CNN oraz warianty tej ostatniej, proponowane podejście MCNN daje większe możliwości rozróżniania i uogólniania podczas klasyfikacji i diagnostyki błędów w przypadku ograniczonej dostępności etykietowanych danych uczących.

Źródło:

Eksploatacja i Niezawodność; 2020, 22, 1; 63-72
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 59.

Tytuł:

Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning
Diagnostyka pittingu kół zębatych na podstawie surowego sygnału emisji akustycznej w oparciu o głębokie uczenie maszynowe

Autorzy:

Li, Xueyi
Li, Jialin
He, David
Qu, Yongzhi

Powiązania:

https://bibliotekanauki.pl/articles/301093.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

gear pitting fault diagnosis
autoencoder
one-dimensional convolutional neural network
acoustic emission signal
diagnostyka pittingu kół zębatych
autoenkoder
jednowymiarowa splotowa sieć neuronowa
sygnał emisji akustycznej

Opis:

Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.
Pitting kół zębatych stanowi jedno z najczęstszych uszkodzeń przekładni mechanicznych. Do wykrywania takich uszkodzeń stosuje się sygnały emisji akustycznej (AE), które, ze względu na niższą wrażliwość na hałas otoczenia, stanowią skuteczniejsze narzędzie diagnostyczne niż tradycyjne sygnały wibracyjne. Wykrywalność zużycia guzełkowatego (pittingu) kół zębatych przy użyciu sygnałów AE i splotowych sieci neuronowych jest jednak niska. Aby rozwiązać ten problem, w niniejszym artykule zaproponowano nową metodę diagnozowania uszkodzeń kół zębatych za pomocą surowych sygnałów AE, którą nazwano augmented convolution sparse autoencoder (konwolucją rozszerzoną z wykorzystaniem autoenkodera rzadkiego, ACSAE). Jest to metoda samouczenia jednowymiarowych splotowych sieci neuronowych realizowanego za pomocą autoenkodera rzadkiego. Metoda ta wykorzystuje teorię wzmocnienia do zwiększania adaptacyjności i odporności sieci. Metoda ACSAE pozwala na automatyczne wyodrębnianie cech degradacji bezpośrednio z oryginalnych sygnałów AE bez konieczności ich konwersji do domeny czasu i częstotliwości. Walidację metody przeprowadzono na podstawie sygnałów AE otrzymanych w badaniach kół zębatych. Analiza wyników badań pittingu kół zębatych wskazuje, że proponowana metoda pozwala na skuteczną detekcję tego typu uszkodzeń, przy wskaźniku wykrywalności powyżej 98%. Analiza porównawcza pokazuje, że metoda ACSAE cechuje się większą trafnością diagnostyczną w wykrywaniu błędów montażowych kół zębatych w porównaniu z sieciami neuronowymi w pełni połączonymi, splotowymi i rekurencyjnymi.

Źródło:

Eksploatacja i Niezawodność; 2019, 21, 3; 403-410
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 60.

Tytuł:

A hybrid approach of a deep learning technique for real-time ECG beat detection

Autorzy:

Patro, Kiran Kumar
Prakash, Allam Jaya
Samantray, Saunak
Pławiak, Joanna
Tadeusiewicz, Ryszard
Pławiak, Paweł

Powiązania:

https://bibliotekanauki.pl/articles/2172118.pdf

Data publikacji:

2022

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

cardiac abnormalities
CAD
convolutional neural network
CNN
deep learning
ECG
electrocardiogram
supra ventricular ectopic beats
SVE
nieprawidłowości kardiologiczne
sieć neuronowa konwolucyjna
uczenie głębokie
EKG
elektrokardiogram

Opis:

This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart’s electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan–Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT–BIH database and a descent accuracy of 99.12% with the real-time acquired database.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2022, 32, 3; 455--465
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 61.

Tytuł:

Hybrid deep learning method for detection of liver cancer

Autorzy:

Deshmukh, Sunita P.
Choudhari, Dharmaveer
Amalraj, Shankar
Matte, Pravin N.

Powiązania:

https://bibliotekanauki.pl/articles/38701864.pdf

Data publikacji:

2023

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

liver cancer detection
deep learning
fully convolutional neural network
hybrid approach
discrete wavelet transform
wykrywanie raka wątroby
uczenie głębokie
neuronowa sieć konwulcyjna
podejście hybrydowe
dyskretna transformata falkowa

Opis:

Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.

Źródło:

Computer Assisted Methods in Engineering and Science; 2023, 30, 2; 151-165
2299-3649

Pojawia się w:

Computer Assisted Methods in Engineering and Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 62.

Tytuł:

Convolutional neural networks in the SSI analysis for mine-induced vibrations

Autorzy:

Zając, Maciej
Kuźniar, Krystyna

Powiązania:

https://bibliotekanauki.pl/articles/38707462.pdf

Data publikacji:

2024

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

deep learning
convolutional neural network
shallow neural network
small data set
soil-structure interaction
mine-induced vibrations
głęboka nauka
splotowa sieć neuronowa
płytka sieć neuronowa
mały zestaw danych
interakcja gleba-struktura
wibracje wywołane minami

Opis:

Deep neural networks (DNNs) have recently become one of the most often used softcomputational tools for numerical analysis. The huge success of DNNs in the field of imageprocessing is associated with the use of convolutional neural networks (CNNs). CNNs,thanks to their characteristic structure, allow for the effective extraction of multi-layerfeatures. In this paper, the application of CNNs to one of the important soil-structureinteraction (SSI) problems, i.e., the analysis of vibrations transmission from the free-field next to a building to the building foundation, is presented in the case of mine-induced vibrations. To achieve this, the dataset from in-situ experimental measurements,containing 1D ground acceleration records, was converted into 2D spectrogram imagesusing either Fourier transform or continuous wavelet transform. Next, these images wereused as input for a pre-trained CNN. The output is a ratio of maximal vibration valuesrecorded simultaneously on the building foundation and on the ground. Therefore, the lastlayer of the CNN had to be changed from a classification to a regression one. The obtainedresults indicate the suitability of CNN for the analyzed problem.

Źródło:

Computer Assisted Methods in Engineering and Science; 2024, 31, 1; 3-28
2299-3649

Pojawia się w:

Computer Assisted Methods in Engineering and Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 63.

Tytuł:

Detection and Localization of Audio Event for Home Surveillance Using CRNN

Autorzy:

Suruthhi, V. S.
Smita, V.
Rolant Gini, J.
Ramachandran, K. I.

Powiązania:

https://bibliotekanauki.pl/articles/2055274.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

convolutional recurrent neural network
CRNN
gated recurrent unit
GRU
long short-term memory
LSTM
sound event localization and detection
SELD

Opis:

Safety and security have been a prime priority in people’s lives, and having a surveillance system at home keeps people and their property more secured. In this paper, an audio surveillance system has been proposed that does both the detection and localization of the audio or sound events. The combined task of detecting and localizing the audio events is known as Sound Event Localization and Detection (SELD). The SELD in this work is executed through Convolutional Recurrent Neural Network (CRNN) architecture. CRNN is a stacked layer of convolutional neural network (CNN), recurrent neural network (RNN) and fully connected neural network (FNN). The CRNN takes multichannel audio as input, extracts features and does the detection and localization of the input audio events in parallel. The SELD results obtained by CRNN with the gated recurrent unit (GRU) and with long short-term memory (LSTM) unit are compared and discussed in this paper. The SELD results of CRNN with LSTM unit gives 75% F1 score and 82.8% frame recall for one overlapping sound. Therefore, the proposed audio surveillance system that uses LSTM unit produces better detection and overall performance for one overlapping sound.

Źródło:

International Journal of Electronics and Telecommunications; 2021, 67, 4; 735--741
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 64.

Tytuł:

Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea

Autorzy:

Kandukuri, Usha Rani
Prakash, Allam Jaya
Patro, Kiran Kumar
Neelapu, Bala Chakravarthy
Tadeusiewicz, Ryszard
Pławiak, Paweł

Powiązania:

https://bibliotekanauki.pl/articles/24200694.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

sleep apnea
convolutional neural network
constant Q-transform
deep learning
single lead ECG signal
non apnea
obstructive sleep apnea
bezdech senny
sieć neuronowa konwolucyjna
uczenie głębokie
sygnał EKG
obturacyjny bezdech senny

Opis:

Obstructive sleep apnea (OSA) is a long-term sleep disorder that causes temporary disruption in breathing while sleeping. Polysomnography (PSG) is the technique for monitoring different signals during the patient’s sleep cycle, including electroencephalogram (EEG), electromyography (EMG), electrocardiogram (ECG), and oxygen saturation (SpO2). Due to the high cost and inconvenience of polysomnography, the usefulness of ECG signals in detecting OSA is explored in this work, which proposes a two-dimensional convolutional neural network (2D-CNN) model for detecting OSA using ECG signals. A publicly available apnea ECG database from PhysioNet is used for experimentation. Further, a constant Q-transform (CQT) is applied for segmentation, filtering, and conversion of ECG beats into images. The proposed CNN model demonstrates an average accuracy, sensitivity and specificity of 91.34%, 90.68% and 90.70%, respectively. The findings obtained using the proposed approach are comparable to those of many other existing methods for automatic detection of OSA.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 3; 493--506
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 65.

Tytuł:

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Autorzy:

Kołodziej, Marcin
Majkowski, Andrzej
Tarnowski, Paweł
Rak, Remigiusz Jan
Rysz, Andrzej

Powiązania:

https://bibliotekanauki.pl/articles/2090741.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

epilepsy
seizure detection
seizure prediction
convolutional neural network
deep learning
ECG
HRV
cardiac sympathetic index
padaczka
wykrywanie napadu
przewidywanie napadu
splotowa sieć neuronowa
głęboka nauka
technika deep learning
EKG
wskaźnik współczulny serca

Opis:

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136921, 1--9
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 66.

Tytuł:

A new method of cardiac sympathetic index estimation using a 1D-convolutional neural network

Autorzy:

Kołodziej, Marcin
Majkowski, Andrzej
Tarnowski, Paweł
Rak, Remigiusz Jan
Rysz, Andrzej

Powiązania:

https://bibliotekanauki.pl/articles/2173565.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

epilepsy
seizure detection
seizure prediction
convolutional neural network
deep learning
ECG
HRV
cardiac sympathetic index
padaczka
wykrywanie napadu
przewidywanie napadu
splotowa sieć neuronowa
głęboka nauka
technika deep learning
EKG
wskaźnik współczulny serca

Opis:

Epilepsy is a neurological disorder that causes seizures of many different types. The article presents an analysis of heart rate variability (HRV) for epileptic seizure prediction. Considering that HRV is nonstationary, our research focused on the quantitative analysis of a Poincare plot feature, i.e. cardiac sympathetic index (CSI). It is reported that the CSI value increases before the epileptic seizure. An algorithm using a 1D-convolutional neural network (1D-CNN) was proposed for CSI estimation. The usability of this method was checked for 40 epilepsy patients. Our algorithm was compared with the method proposed by Toichi et al. The mean squared error (MSE) for testing data was 0.046 and the mean absolute percentage error (MAPE) amounted to 0.097. The 1D-CNN algorithm was also compared with regression methods. For this purpose, a classical type of neural network (MLP), as well as linear regression and SVM regression, were tested. In the study, typical artifacts occurring in ECG signals before and during an epileptic seizure were simulated. The proposed 1D-CNN algorithm estimates CSI well and is resistant to noise and artifacts in the ECG signal.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136921
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 67.

Tytuł:

Application of convolutional neuron network for image processing and interpretation
Zastosowanie konwolucyjnych sieci neuronowych do przetwarzania i interpretacji obrazów

Autorzy:

Pałczyński, Krzysztof

Powiązania:

https://bibliotekanauki.pl/articles/2016320.pdf

Data publikacji:

2020

Wydawca:

Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB

Tematy:

convolutional neural network
image processing
filtration
convolution
activation function
loss function
softmax
cross entropy
L2
Dropout
stochastic gradient drop
konwolucyjne sieci neuronowe
przetwarzanie obrazów
filtracja
splot
funkcja aktywacji
funkcja straty
entropia krzyżowa
stochastyczny spadek gradientu

Opis:

This article describes the application of Convolutional Neural Network in image processing and describes how it works. There are presented: network layers, types of activation functions, example of the AlexNet network architecture, the use of the loss function and the cross entropy method to calculate the loss during tests, L2 and Dropout methods used for weights regularization and optimization of the loss function using Stochastic Gradient Drop.
Artykuł ten opisuje zastosowanie Konwolucyjnych Sieci Neuronowych w przetwarzaniu obrazów. W celu lepszego zrozumienia tematu opisano sposób działania sieci. Przedstawiono sieci wielowarstwowe, rodzaje funkcji aktywacji, przykład architektury sieci AlexNet. W artykule skupiono się na opisaniu wykorzystania funkcji straty oraz metody entropii krzyżowej do obliczenia straty w czasie testów. Opisano również sposoby normalizacji wag L2 i Dropout oraz optymalizację funkcji straty za pomocą Stochastycznego Spadku Gradientu.

Źródło:

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2019, 23; 5-12
1899-0088

Pojawia się w:

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

Dostawca treści:

Biblioteka Nauki

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Skocz do pozycji: 68.

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

/5

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Skocz do pozycji: 69.

Tytuł:

Deep Learning Can Improve Early Skin Cancer Detection

Autorzy:

Mohamed, Abeer
Mohamed, Wael A.
Zekry, Abdel Halim

Powiązania:

https://bibliotekanauki.pl/articles/963798.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

technology
dermoscopic lesions
convolutional
neural network
ISIC dataset
deep learning
neural networks

Opis:

Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.

Źródło:

International Journal of Electronics and Telecommunications; 2019, 65, 3; 507-512
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

Artykuł

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Skocz do pozycji: 70.

Tytuł:

The concept of an intelligent system of an outfit completion

Autorzy:

Semianchuk, Sofiia
Shestakevych, Tetiana

Powiązania:

https://bibliotekanauki.pl/articles/1833886.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Oddział w Lublinie PAN

Tematy:

neural network
wardrobe
pattern recognition
image classification
convolutional neural networks
Analytic Hierarchy Process

Opis:

The article considers the main criteria for the selection and formation of the wardrobe, which is one of the areas of application of methods and means for image classification. Typical software solutions for the task are analyzed, and the Analytic Hierarchy Process was used to analyze such applications. To improve the wardrobe selection process, the concept of an intelligent information system based on the use of convolutional neural networks was proposed.

Źródło:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2020, 9, 2; 30--36
2084-5715

Pojawia się w:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

Dostawca treści:

Biblioteka Nauki

/5

Artykuł

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Skocz do pozycji: 71.

Tytuł:

The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks

Autorzy:

Chu, J. L.
Krzyżak, A.

Powiązania:

https://bibliotekanauki.pl/articles/91650.pdf

Data publikacji:

2014

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

neural networks
belief networks
convolutional neural networks
artificial neural networks
Deep Belief Network
generative model

Opis:

Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 5-19
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 72.

Tytuł:

Gramian angular field transformation-based intrusion detection

Autorzy:

Terzi, Duygu Sinanc

Powiązania:

https://bibliotekanauki.pl/articles/27312895.pdf

Data publikacji:

2022

Wydawca:

Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH

Tematy:

encoding intrusions as images
convolutional neural networks
Gramian angular fields
intrusion detection
network security

Opis:

Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain – involving only data-image conversion and classification. This shows the power of simply changing the problem space.

Źródło:

Computer Science; 2022, 23 (4); 571--585
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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