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Tytuł:
Cognitive Modeling and Formation of the Knowledge Base of the Information System for Assessing the Rating of Enterprises
Autorzy:
Kryvoruchko, Olena
Desiatko, Alona
Karpunin, Igor
Hnatchenko, Dmytro
Lakhno, Myroslav
Malikova, Feruza
Turdaliev, Ayezhan
Powiązania:
https://bibliotekanauki.pl/articles/27311936.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
information security
audit
Bayesian network
artificial neural networks
Opis:
A mathematical model is proposed that makes it possible to describe in a conceptual and functional aspect the formation and application of a knowledge base (KB) for an intelligent information system (IIS). This IIS is developed to assess the financial condition (FC) of the company. Moreover, for circumstances related to the identification of individual weakly structured factors (signs). The proposed model makes it possible to increase the understanding of the analyzed economic processes related to the company's financial system. An iterative algorithm for IIS has been developed that implements a model of cognitive modeling. The scientific novelty of the proposed approach lies in the fact that, unlike existing solutions, it is possible to adjust the structure of the algorithm depending on the characteristics of a particular company, as well as form the information basis for the process of assessing the company's FC and the parameters of the cognitive model.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 697--705
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Cascade Feed Forward Neural Network-based Model for Air Pollutants Evaluation of Single Monitoring Stations in Urban Areas
Autorzy:
Capizzi, G.
Lo Sciuto, G.
Monforte, P.
Napoli, C.
Powiązania:
https://bibliotekanauki.pl/articles/226736.pdf
Data publikacji:
2015
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
Synthetic Aperture Radar (SAR)
mahalanobis distance
Opis:
In this paper, air pollutants concentrations for NO2, NO, NOx and PM10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy.
Źródło:
International Journal of Electronics and Telecommunications; 2015, 61, 4; 327-332
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automation of Information Security Risk Assessment
Autorzy:
Akhmetov, Berik
Lakhno, Valerii
Chubaievskyi, Vitalyi
Kaminskyi, Serhii
Adilzhanova, Saltanat
Ydyryshbayeva, Moldir
Powiązania:
https://bibliotekanauki.pl/articles/2124744.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information security
audit
Bayesian network
artificial neural networks
Opis:
An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DCS.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 3; 549--555
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Classification of EEG Signals Using Quantum Neural Network and Cubic Spline
Autorzy:
Abdul-Zahra Raheem, M.
AbdulRazzaq Hussein, E.
Powiązania:
https://bibliotekanauki.pl/articles/227206.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
signals
ERP signals
cubic spline
neural networks
quantum neural network
Opis:
The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.
Źródło:
International Journal of Electronics and Telecommunications; 2016, 62, 4; 401-408
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Localization technique of IoT Nodes Using Artificial Neural Networks (ANN)
Autorzy:
Krupanek, Beata
Bogacz, Ryszard
Powiązania:
https://bibliotekanauki.pl/articles/1844456.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wireless networks
node localization
location errors
WSN
IoT
neural networks
Opis:
One of the ways to improve calculations related to determining the position of a node in the IoT measurement system is to use artificial neural networks (ANN) to calculate coordinates. The method described in the article is based on the measurement of the RSSI (Received Signal Strength Indicator), which value is then processed by the neural network. Hence, the proposed system works in two stages. In the first stage, RSSI coefficient samples are taken, and then the node location is determined on an ongoing basis. Coordinates anchor nodes (i.e. sensors with fixed and previously known positions) and the matrix of RSSI coefficients are used in the learning process of the neural network. Then the RSSI matrix determined for the system in which the nodes with unknown positions are located is fed into the neural network inputs. The result of the work is a system and algorithm that allows determining the location of the object without processing data separately in nodes with low computational performance.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 4; 769-774
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Voice recognition through the use of Gabor transform and heuristic algorithm
Autorzy:
Woźniak, M.
Połap, D.
Powiązania:
https://bibliotekanauki.pl/articles/226687.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
voice recognition
Gabor transform
heuristic algorithm
swarm
Opis:
Increasingly popular use of verification methods based on specific characteristics of people like eyeball, fingerprint or voice makes inventing more accurate and irrefutable methods of that urgent. In this work we present voice verification based on Gabor transformation. Proposed approach involves creation of spectrogram, which serves as a habitat for the population in selected heuristic algorithm. The use of heuristic allows for feature extraction to enable identity verification using classical neural network. The results of the research are presented and discussed to show efficiency of the proposed methodology.
Źródło:
International Journal of Electronics and Telecommunications; 2017, 63, 2; 159-164
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Flexible Neural Network Architecture for Handwritten Signatures Recognition
Autorzy:
Połap, D.
Woźniak, M.
Powiązania:
https://bibliotekanauki.pl/articles/226883.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
neural networks
handwritten signatures preprocessing
signature recognition
chebyshev polynomials
Opis:
This article illustrates modeling of flexible neural networks for handwritten signatures preprocessing. An input signature is interpolated to adjust inclination angle, than descriptor vector is composed. This information is preprocessed in proposed flexible neural network architecture, in which some neurons are becoming crucial for recognition and adapt to classification purposes. Experimental research results are compared in benchmark tests with classic approach to discuss efficiency of proposed solution.
Źródło:
International Journal of Electronics and Telecommunications; 2016, 62, 2; 197-202
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
Autorzy:
Nayak, Sachin
Vincent, Shweta
Sumathi, K.
Kumar, Om Prakash
Pathan, Sameena
Powiązania:
https://bibliotekanauki.pl/articles/2055258.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classification
Convolutional Neural Networks
Ensemble Learning
machine learning
metadata
Opis:
Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Overcoming Localization Errors due to Node Power Drooping in a Wireless Sensor Network
Autorzy:
Khan, S. A.
Daachi, B.
Djouani, K.
Powiązania:
https://bibliotekanauki.pl/articles/226280.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
wireless sensor networks
RSSI
localization
range-free scheme
energy considerations
neural networks
Opis:
Received Signal Strength Indication (RSSI) plays a vital role in the range-free localization of sensor nodes in a wireless sensor network and a good amount of research has been made in this regard. One important factor is the battery voltage of the nodes (i.e., the MICAz sensors) which is not taken into account in the existing literature. As battery voltage level performs an indispensable role for the position estimation of sensor nodes through anchor nodes therefore, in this paper, we take into a account this crucial factor and propose an algorithm that overcomes the problem of decaying battery. We show the results, in terms of more precise localization of sensor nodes through simulation. This work is an extension to [1] and now we also use neural network to overcome the localization errors generated due to gradual battery voltage drooping.
Źródło:
International Journal of Electronics and Telecommunications; 2011, 57, 3; 341-346
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
MIMO Beam Selection in 5G Using Neural Networks
Autorzy:
Ruseckas, Julius
Molis, Gediminas
Bogucka, Hanna
Powiązania:
https://bibliotekanauki.pl/articles/2055220.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
5G
context information
MIMO beam orientation
machine learning
neural networks
Opis:
In this paper, we consider cell-discovery problem in 5G millimeter-wave (mmWave) communication systems using multiple input, multiple output (MIMO) beam-forming technique. Specifically, we aim at the proper beam selection method using context-awareness of the user-equipment to reduce latency in beam/cell identification. Due to high path-loss in mmWave systems, beam-forming technique is extensively used to increase Signal-to-Noise Ratio (SNR). When seeking to increase user discovery distance, narrow beam must be formed. Thus, a number of possible beam orientations and consequently time needed for the discovery increases significantly when random scanning approach is used. The idea presented here is to reduce latency by employing artificial intelligence (AI) or machine learning (ML) algorithms to guess the best beam orientation using context information from the Global Navigation Satellite System (GNSS), lidars and cameras, and use the knowledge to swiftly initiate communication with the base station. To this end, here, we propose a simple neural network to predict beam orientation from GNSS and lidar data. Results show that using only GNSS data one can get acceptable performance for practical applications. This finding can be useful for user devices with limited processing power.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 4; 693--698
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Robust CNN Model for Diagnosis of COVID-19 Based on CT Scan Images and DL Techniques
Autorzy:
Eldeeb, Ahmed H.
Amr, Mohammed Nagah
Ibrahim, Amin S.
Kamel, Hesham
Fouad, Sara
Powiązania:
https://bibliotekanauki.pl/articles/2200729.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Deep learning
COVID-19
Artificial Intelligence
computed tomography
Convolutional Neural Networks
Opis:
The 2019 Coronavirus (COVID-19) virus has caused damage on people's respiratory systems over the world. Computed Tomography (CT) is a faster complement for RT-PCR during peak virus spread times. Nowadays, Deep Learning (DL) with CT provides more robust and reliable methods for classifying patterns in medical pictures. In this paper, we proposed a simple low training proposed customized Convolutional Neural Networks (CNN) customized model based on CNN architecture that layers which are optionals may be included such as the layer of batch normalization to reduce time taken for training and a layer with a dropout to deal with overfitting. We employed a huge dataset of chest CT slices images from diverse sources COVIDx-CT, which consists of a 16,146-image dataset with 810 patients of various nationalities. The proposed customized model's classification results compared to the VGG-16, Alex Net, and ResNet50 Deep Learning models. The proposed CNN model shows robustness by achieving an overall accuracy of 93% compared to 88%, 89%, and 95% for the VGG-16, Alex Net, and ResNet50 DL models for the classification of 3 classes. When this relates to binary classification, the classification accuracy of the proposed model and the VGG-16 models were identical (almost 100% accurate), with 0.17% of misclassification in the class of Non-Covid-19, the Alex Net model achieved almost 100% classification accuracy with 0.33% misclassification in the class of Non-Covid-19. Finally, ResNet50 achieved 95% classification accuracy with 5% misclassification in the Non-Covid-19 class.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 4; 731--739
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimization of Animal Detection in Thermal Images Using YOLO Architecture
Autorzy:
Popek, Łukasz
Perz, Rafał
Galiński, Grzegorz
Abratański, Artur
Powiązania:
https://bibliotekanauki.pl/articles/27311963.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
artificial neural networks
YOLOv5
transfer learning
genetic algorithm
thermal imaging
Opis:
The article presents research on animal detection in thermal images using the YOLOv5 architecture. The goal of the study was to obtain a model with high performance in detecting animals in this type of images, and to see how changes in hyperparameters affect learning curves and final results. This manifested itself in testing different values of learning rate, momentum and optimizer types in relation to the model’s learning performance. Two methods of tuning hyperparameters were used in the study: grid search and evolutionary algorithms. The model was trained and tested on an in-house dataset containing images with deer and wild boars. After the experiments, the trained architecture achieved the highest score for Mean Average Precision (mAP) of 83%. These results are promising and indicate that the YOLO model can be used for automatic animal detection in various applications, such as wildlife monitoring, environmental protection or security systems.
Źródło:
International Journal of Electronics and Telecommunications; 2023, 69, 4; 826--831
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Information Technologies for Assessing the Quality of IT-specialties Graduates Training of University by Means of Fuzzy Logic and Neural Networks
Autorzy:
Azarova, Anzhelika O.
Azarova, Larysa E.
Pavlov, Sergii V.
Savina, Nataliia B.
Kaplun, Iryna S.
Wójcik, Waldemar
Smailova, Saule
Kalizhanova, Aliya
Powiązania:
https://bibliotekanauki.pl/articles/227142.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information technologies
fuzzy logic
neural networks
quality of IT-specialties graduates' training
Opis:
The information technologies for assessing the quality of IT-specialties graduates' training of university by means of fuzzy logic and neural networks are developed in the article. It makes possible taking into account a wide set of estimation and output parameters, influence of the external and internal factors and allows to simplify the assessing process by means of modern mathematical apparatuses of artificial intelligence.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 3; 411-416
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep Image Features in Music Information Retrieval
Autorzy:
Gwardys, G.
Grzywczak, D.
Powiązania:
https://bibliotekanauki.pl/articles/226400.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
music information retrieval
deep learning
genre classification
convolutional neural networks
transfer learning
Opis:
Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation 3D volumetric images and action recognition in videos. CNNs are able to learn input data representation, instead of using fixed engineered features. In this study, the image model trained on CNN were applied to a Music Information Retrieval (MIR), in particular to musical genre recognition. The model was trained on ILSVRC-2012 (more than 1 million natural images) to perform image classification and was reused to perform genre classification using spectrograms images. Harmonic/percussive separation was applied, because it is characteristic for musical genre. At final stage, the evaluation of various strategies of merging Support Vector Machines (SVMs) was performed on well known in MIR community - GTZAN dataset. Even though, the model was trained on natural images, the results achieved in this study were close to the state-of-the-art.
Źródło:
International Journal of Electronics and Telecommunications; 2014, 60, 4; 321-326
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 22

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