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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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
Tomato disease detection model based on densenet and transfer learning
Autorzy:
Bakr, Mahmoud
Abdel-Gaber, Sayed
Nasr, Mona
Hazman, Maryam
Powiązania:
https://bibliotekanauki.pl/articles/2097440.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
leaf disease detection
convolutional neural network
deep learning
transfer learning
Opis:
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Źródło:
Applied Computer Science; 2022, 18, 2; 56--70
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
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
Artykuł
Tytuł:
The recognition of partially occluded objects with support vector machines, convolutional neural networks and deep belief networks
Autorzy:
Chu, J. L.
Krzyżak, A.
Powiązania:
https://bibliotekanauki.pl/articles/91650.pdf
Data publikacji:
2014
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
neural networks
belief networks
convolutional neural networks
artificial neural networks
Deep Belief Network
generative model
Opis:
Biologically inspired artificial neural networks have been widely used for machine learning tasks such as object recognition. Deep architectures, such as the Convolutional Neural Network, and the Deep Belief Network have recently been implemented successfully for object recognition tasks. We conduct experiments to test the hypothesis that certain primarily generative models such as the Deep Belief Network should perform better on the occluded object recognition task than purely discriminative models such as Convolutional Neural Networks and Support Vector Machines. When the generative models are run in a partially discriminative manner, the data does not support the hypothesis. It is also found that the implementation of Gaussian visible units in a Deep Belief Network trained on occluded image data allows it to also learn to effectively classify non-occluded images.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2014, 4, 1; 5-19
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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