Skocz do pozycji: 1.

Tytuł:

Adaptive Rider Feedback Artificial Tree Optimization-Based Deep Neuro-Fuzzy Network for Classification of Sentiment Grade

Autorzy:

Jasti, Sireesha
Kumar, G.V.S. Raj

Powiązania:

https://bibliotekanauki.pl/articles/2200961.pdf

Data publikacji:

2023

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

deep learning network
feedback artificial tree
natural language processing (NLP)
rider optimization algorithm
sentiment grade classification

Opis:

Sentiment analysis is an efficient technique for expressing users’ opinions (neutral, negative or positive) regarding specific services or products. One of the important benefits of analyzing sentiment is in appraising the comments that users provide or service providers or services. In this work, a solution known as adaptive rider feedback artificial tree optimization-based deep neuro-fuzzy network (RFATO-based DNFN) is implemented for efficient sentiment grade classification. Here, the input is pre-processed by employing the process of stemming and stop word removal. Then, important factors, e.g. SentiWordNet-based features, such as the mean value, variance, as well as kurtosis, spam word-based features, term frequency-inverse document frequency (TF-IDF) features and emoticon-based features, are extracted. In addition, angular similarity and the decision tree model are employed for grouping the reviewed data into specific sets. Next, the deep neuro-fuzzy network (DNFN) classifier is used to classify the sentiment grade. The proposed adaptive rider feedback artificial tree optimization (A-RFATO) approach is utilized for the training of DNFN. The A-RFATO technique is a combination of the feedback artificial tree (FAT) approach and the rider optimization algorithm (ROA) with an adaptive concept. The effectiveness of the proposed A-RFATO-based DNFN model is evaluated based on such metrics as sensitivity, accuracy, specificity, and precision. The sentiment grade classification method developed achieves better sensitivity, accuracy, specificity, and precision rates when compared with existing approaches based on Large Movie Review Dataset, Datafiniti Product Database, and Amazon reviews.

Źródło:

Journal of Telecommunications and Information Technology; 2023, 1; 37--50
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: 2.

Tytuł:

Financing of distance learning in rural areas by the European Social Fund

Autorzy:

Kowalska, I.

Powiązania:

https://bibliotekanauki.pl/articles/572366.pdf

Data publikacji:

2007

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie

Tematy:

financing
distance learning
learning
rural area
European Social Fund
structural fund
fund
European Union
Common Agricultural Policy
education
Internet
computer
learning network
learning scheme

Opis:

The paper is an attempt at evaluating the degree of accuracy of realization of the project: distance learning centres in rural areas financed from the European Social Fund in the frame of the priority II: Development of knowledge-based society, action 2.1.: Broadening the access to education – promotion of lifelong learning; scheme a: decreasing educational inequalities between urban and rural areas. The aims of the project in question include: 1. Creation and equipment with computers and internet connection, of at least 250 distance learning centres in rural areas (in order to enable the final beneficiaries to use the available distance learning programs) 2. Employment in the centres of qualified staff whose task would be to help using the centre’s resources. 3. Creation by the draughtsman, of a countrywide network of distance learning centres using the existing IT infrastructure with units running distance learning. 4. Enabling the final beneficiaries to complement or increase the level of education in the form of distance learning especially at the post-gymnasium level.

Źródło:

Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego; 2007, 01(16)
2081-6960

Pojawia się w:

Zeszyty Naukowe Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie. Problemy Rolnictwa Światowego

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

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: 4.

Tytuł:

E-learning 2.0 Per apprendere e insegnare l’italiano L2: I social network, Facebook e le tecniche didattiche
E-learning 2.0 to learn and teach Italian as a second language: social network, Facebook and language activities

Autorzy:

Cotroneo, Emanuela

Powiązania:

https://bibliotekanauki.pl/articles/446410.pdf

Data publikacji:

2013-12-31

Wydawca:

Wydawnictwo Adam Marszałek

Tematy:

Web 2.0, e-learning 2.0, social network, Facebook, Italian, informal
learning

Opis:

Nowadays it would not be possible to plan an Italian language and culture class without considering the opportunity to use ICT. After the evolution from web to web 2.0 and from e-learning to e-learning 2.0, teachers approached a new concept of learning and teaching online. Web 2.0 tools, the social networks in particular, are considered a valuable resource for communicating, interacting and sharing linguistic and cultural contents, in formal and informal learning. Facebook, the most famous social network, becomes a virtual learning environment for students of Italian as a Second Language where posts, images, links and videos can be shared and used as L2 input. The Facebook page Lingua Italiana Per Stranieri (progetto LIPS), that we described it in this article, represents an example of a didactic use of this famous social network.

Źródło:

Italica Wratislaviensia; 2013, 4; 37-57
2084-4514

Pojawia się w:

Italica Wratislaviensia

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 5.

Tytuł:

Fast computational approach to the Levenberg-Marquardt algorithm for training feedforward neural networks

Autorzy:

Bilski, Jarosław
Smoląg, Jacek
Kowalczyk, Bartosz
Grzanek, Konrad
Izonin, Ivan

Powiązania:

https://bibliotekanauki.pl/articles/2201329.pdf

Data publikacji:

2023

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

feed-forward neural network
neural network learning algorithm
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation

Opis:

This paper presents a parallel approach to the Levenberg-Marquardt algorithm (LM). The use of the Levenberg-Marquardt algorithm to train neural networks is associated with significant computational complexity, and thus computation time. As a result, when the neural network has a big number of weights, the algorithm becomes practically ineffective. This article presents a new parallel approach to the computations in Levenberg-Marquardt neural network learning algorithm. The proposed solution is based on vector instructions to effectively reduce the high computational time of this algorithm. The new approach was tested on several examples involving the problems of classification and function approximation, and next it was compared with a classical computational method. The article presents in detail the idea of parallel neural network computations and shows the obtained acceleration for different problems.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2023, 13, 2; 45--61
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: 6.

Tytuł:

Local Levenberg-Marquardt algorithm for learning feedforwad neural networks

Autorzy:

Bilski, Jarosław
Kowalczyk, Bartosz
Marchlewska, Alina
Zurada, Jacek M.

Powiązania:

https://bibliotekanauki.pl/articles/1837415.pdf

Data publikacji:

2020

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

feed-forward neural network
neural network learning algorithm
optimization problem
Levenberg-Marquardt algorithm
QR decomposition
Givens rotation

Opis:

This paper presents a local modification of the Levenberg-Marquardt algorithm (LM). First, the mathematical basics of the classic LM method are shown. The classic LM algorithm is very efficient for learning small neural networks. For bigger neural networks, whose computational complexity grows significantly, it makes this method practically inefficient. In order to overcome this limitation, local modification of the LM is introduced in this paper. The main goal of this paper is to develop a more complexity efficient modification of the LM method by using a local computation. The introduced modification has been tested on the following benchmarks: the function approximation and classification problems. The obtained results have been compared to the classic LM method performance. The paper shows that the local modification of the LM method significantly improves the algorithm’s performance for bigger networks. Several possible proposals for future works are suggested.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 4; 299-316
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:

Deep reinforcement learning overview of the state of the art

Autorzy:

Fenjiro, Y.
Benbrahim, H.

Powiązania:

https://bibliotekanauki.pl/articles/384788.pdf

Data publikacji:

2018

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

reinforcement learning
deep learning
convolutional network
recurrent network
deep reinforcement learning

Opis:

Artificial intelligence has made big steps forward with reinforcement learning (RL) in the last century, and with the advent of deep learning (DL) in the 90s, especially, the breakthrough of convolutional networks in computer vision field. The adoption of DL neural networks in RL, in the first decade of the 21 century, led to an end-toend framework allowing a great advance in human-level agents and autonomous systems, called deep reinforcement learning (DRL). In this paper, we will go through the development Timeline of RL and DL technologies, describing the main improvements made in both fields. Then, we will dive into DRL and have an overview of the state-ofthe- art of this new and promising field, by browsing a set of algorithms (Value optimization, Policy optimization and Actor-Critic), then, giving an outline of current challenges and real-world applications, along with the hardware and frameworks used. In the end, we will discuss some potential research directions in the field of deep RL, for which we have great expectations that will lead to a real human level of intelligence.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 3; 20-39
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

Network Traffic Classification in an NFV Environment using Supervised ML Algorithms

Autorzy:

Ilievski, Gjorgji
Latkoski, Pero

Powiązania:

https://bibliotekanauki.pl/articles/1839335.pdf

Data publikacji:

2021

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

classification
machine learning
network functions virtualization
network traffic

Opis:

We have conducted research on the performance of six supervised machine learning (ML) algorithms used for network traffic classification in a virtual environment driven by network function virtualization (NFV). The performance-related analysis focused on the precision of the classification process, but also in time-intensity (speed) of the supervised ML algorithms. We devised specific traffic taxonomy using commonly used categories, with particular emphasis placed on VoIP and encrypted VoIP protocols serve as a basis of the 5G architecture. NFV is considered to be one of the foundations of 5G development, as the traditional networking components are fully virtualized, in many cases relaying on mixed cloud solutions, both of the premise- and public cloud-based variety. Virtual machines are being replaced by containers and application functions while most of the network traffic is flowing in the east-west direction within the cloud. The analysis performed has shown that in such an environment, the Decision Tree algorithm is best suited, among the six algorithms considered, for performing classification-related tasks, and offers the required speed that will introduce minimal delays in network flows, which is crucial in 5G networks, where packet delay requirements are of great significance. It has proven to be reliable and offered excellent overall performance across multiple network packet classes within a virtualized NFV network architecture. While performing the classification procedure, we were working only with the statistical network flow features, leaving out packet payload, source, destination- and port-related information, thus making the analysis valid not only from the technical, but also from the regulatory point of view.

Źródło:

Journal of Telecommunications and Information Technology; 2021, 3; 23-31
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: 9.

Tytuł:

Ensemble Model for Network Intrusion Detection System Based on Bagging Using J48

Autorzy:

Otoom, Mohammad Mahmood
Sattar, Khalid Nazim Abdul
Al Sadig, Mutasim

Powiązania:

https://bibliotekanauki.pl/articles/2201908.pdf

Data publikacji:

2023

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

cyber security
network intrusion
ensemble learning
machine learning
ML

Opis:

Technology is rising on daily basis with the advancement in web and artificial intelligence (AI), and big data developed by machines in various industries. All of these provide a gateway for cybercrimes that makes network security a challenging task. There are too many challenges in the development of NID systems. Computer systems are becoming increasingly vulnerable to attack as a result of the rise in cybercrimes, the availability of vast amounts of data on the internet, and increased network connection. This is because creating a system with no vulnerability is not theoretically possible. In the previous studies, various approaches have been developed for the said issue each with its strengths and weaknesses. However, still there is a need for minimal variance and improved accuracy. To this end, this study proposes an ensemble model for the said issue. This model is based on Bagging with J48 Decision Tree. The proposed models outperform other employed models in terms of improving accuracy. The outcomes are assessed via accuracy, recall, precision, and f-measure. The overall average accuracy achieved by the proposed model is 83.73%.

Źródło:

Advances in Science and Technology. Research Journal; 2023, 17, 2; 322--329
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

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: 11.

Tytuł:

Wykorzystanie sieci bayesowskich do prognozowania bankructwa firm
Bankruptcy prediction with Bayesian networks

Autorzy:

Gąska, Damian

Powiązania:

https://bibliotekanauki.pl/articles/434020.pdf

Data publikacji:

2016

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

bankruptcy prediction
Bayesian network
structure learning

Opis:

The aim of the paper is to compare accuracy of some bankruptcy prediction models based on Bayesian networks. Some network structure learning algorithms were analyzed as a tool for classifiers construction. Empirical analysis was applied to companies listed on Warsaw Stock Exchange. The paper gives short overview of theoretical background behind discussed issues and presents results of empirical analysis.

Źródło:

Śląski Przegląd Statystyczny; 2016, 14 (20); 131-144
1644-6739

Pojawia się w:

Śląski Przegląd Statystyczny

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

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: 13.

Tytuł:

EEULA: an energy-aware event-driven unicast algorithm for wireless sensor network by learning automata

Autorzy:

Kashani, A. A. S.
Noori, A. M. M.

Powiązania:

https://bibliotekanauki.pl/articles/102180.pdf

Data publikacji:

2016

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

network lifetime
wireless sensor network
learning automata
energy efficiency algorithm

Opis:

Energy consumption is one of the major challenges in wireless sensor networks, thus necessitating an approach for its minimization and for load balancing data. The network lifetime ends with the death of one of its nodes, which, in turn, causes energy depletion in and partition of the network. Furthermore, the total energy consumption of nodes depends on their location; that is, because of the loaded data, energy discharge in the nodes close to the base station occurs faster than other nodes, the model presented here, through using learning automata, selects the path appropriate for data transferring; the selected path is rewarded or penalized taking the reaction of surrounding paths into account. We have used learning automata for energy management in finding the path; the routing protocol was simulated by NS2 simulator; the lifetime, energy consumption and balance in an event-driven network in our proposed method were compared with other algorithms.

Źródło:

Advances in Science and Technology. Research Journal; 2016, 10, 30; 9-18
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

Intrusion Detection in Software Defined Networks with Self-organized Maps

Autorzy:

Jankowski, D.
Amanowicz, M.

Powiązania:

https://bibliotekanauki.pl/articles/308109.pdf

Data publikacji:

2015

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

IDS dataset
machine learning
metasploit
network security
network simulation
OpenFlow
virtualization

Opis:

The Software Defined Network (SDN) architecture provides new opportunities to implement security mechanisms in terms of unauthorized activities detection. At the same time, there are certain risks associated with this technology. The presented approach covers a conception of the measurement method, virtual testbed and classification mechanism for SDNs. The paper presents a measurement method which allows collecting network traffic flow parameters, generated by a virtual SDN environment. The collected dataset can be used in machine learning methods to detect unauthorized activities.

Źródło:

Journal of Telecommunications and Information Technology; 2015, 4; 3-9
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: 15.

Tytuł:

Exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller

Autorzy:

Pan, L.

Powiązania:

https://bibliotekanauki.pl/articles/260396.pdf

Data publikacji:

2018

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

adaptive neural network
marine resources
learning robot

Opis:

To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.

Źródło:

Polish Maritime Research; 2018, S 3; 78-83
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Development of an automated assembly process supported with an artificial neural network

Autorzy:

Bobka, P.
Heyn, J.
Henningson, J.-O.
Römer, M.
Engbers, T.
Dietrich, F.
Dröder, K.

Powiązania:

https://bibliotekanauki.pl/articles/99408.pdf

Data publikacji:

2018

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

assembly
machine learning
neural network
industrial robot

Opis:

A central problem in automated assembly is the ramp-up phase. In order to achieve the required tolerances and cycle times, assembly parameters must be determined by extensive manual parameter variations. Therefore, the duration of the ramp-up phase represents a planning uncertainty and a financial risk, especially when high demands are placed on dynamics and precision. To complete this phase as efficiently as possible, comprehensive planning and experienced personnel are necessary. In this paper, we examine the use of machine learning techniques for the ramp-up of an automated assembly process. Specifically we use a deep artificial neural network to learn process parameters for pick-and-place operations of planar objects. We describe how the handling parameters of an industrial robot can be adjusted and optimized automatically by artificial neural networks and examine this approach in laboratory experiments. Furthermore, we test whether an artificial neural network can be used to optimize assembly parameters in process as an adaptive process controller. Finally, we discuss the advantages and disadvantages of the described approach for the determination of optimal assembly parameters in the ramp-up phase and during the utilization phase.

Źródło:

Journal of Machine Engineering; 2018, 18, 3; 28-41
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

Феномен соціальних мереж: парадокс залежності та варіативність моделювання
The phenomenon of social networks: the paradox of dependence and variability modelling

Autorzy:

Hrybiuk, Olena

Powiązania:

https://bibliotekanauki.pl/articles/469894.pdf

Data publikacji:

2017-03-01

Wydawca:

Wyższa Szkoła Gospodarki Euroregionalnej im. Alcide De Gasperi w Józefowie

Tematy:

modeling
variable models
computer-oriented learning environment
mathematics
network
engineering
social network

Opis:

Purpose. Іn the publication, a comparative analysis of scientifically-methodical bases of modeling of the learning environment, including using social networking was shown. Variable models are represented on the basis of competence-based approach taking into account the main stages of the design. Methods. Theoretical (the analysis of philosophical, psychological, sociological, pedagogical and methodological publications on the subject of the study); the empirical (observation, survey, pedagogical experiment); methods of statistical data processing. Results. To improve efficiency, including using social networking, it is recommended to consider the advantages of decentralized and centralized networks, improving the sustainability of horizontal communication, transparency, access to resources, monitoring of "reliability ratings", limiting the number of network members, the formation and development of "competence networks". Network stability is maintained even in case of instability of its membership and transaction volume. Based on the comparative analysis of the models considered in the present research, the principles of formation and coordination of the organizational structure in the virtual space. Differences due to the specifics regarding the use of modern information and cognitive technology, the inner logic of action and the specific culture of communication in the network. On the basis of similarities taking into account the analysed models, the prospect for further research is seen in the context of further ways to improve the efficiency of agents in the virtual space, including social networks.

Źródło:

Intercultural Communication; 2017, 2, 1; 123-142
2451-0998

Pojawia się w:

Intercultural Communication

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

Report on the Implementation of WP3 “Analyses and Evaluation of the ICT Level, E-learning and Intercultural Development in Every Participating Country” in the Framework of the IRNet Project

Autorzy:

Smyrnova-Trybulska, Eugenia
Ogrodzka-Mazur, Ewa
Szafrańska-Gajdzica, Anna
Doluk, Ewelina
Noskova, Tatiana
Pavlova, Tatiana
Yakovleva, Olga
Morze, Nataliia
Kommers, Piet
Pinto, Paulo
Díaz, Laura Alonso
Yuste Tosina, Rocio
Gutiérrez Esteban, Prudencia
Cápay, Martin
Drlík, Martin
Malach, Josef
Issa, Tomayess
Issa, Theodora
Romaniukha, Maryna

Powiązania:

https://bibliotekanauki.pl/articles/1366007.pdf

Data publikacji:

2017-04-01

Wydawca:

Wydawnictwo Uniwersytetu Śląskiego

Tematy:

International Research Network IRNet
ICT
E-learning
Competencies

Opis:

This article, prepared by an international team of authors - researchers from different scientific areas, connected with ICT, e-learning, pedagogy, and other related disciplines - focuses on the objectives and some results of the IRNet international project. In particular, this article describes the research tools, methods and some procedures of the WP3 “Analyses and Evaluation of the ICT Level, E-learning and Intercultural Development in Every Participating Country”: Objec- tives, Tasks, Deliverables, and implementation of research trips. Except that, the article presents more important events, such as (video)conferences, semi- nars, workshops, an e-round table debate; among these events are ICTE2014, DLCC2014, “New Educational Strategies in Modern Information Space,” “High- tech information educational environment,” during which some more important results of the project research were presented. The list of publications includes 32 papers and a manuscript with WP3 results. Researchers from Poland, Russia, Ukraine, the Netherlands, Spain, Slovakia, Portugal, Czech Republic and Australia analysed the results of WP3 in the context of the next stages and Work packages of IRNet project - International Research Network.

Źródło:

International Journal of Research in E-learning IJREL; 2015, 1, 2; 123-142
2451-2583
2543-6155

Pojawia się w:

International Journal of Research in E-learning IJREL

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

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: 20.

Tytuł:

Wpływ opisu danych na efektywność uczenia oraz pracy sztucznej sieci neuronowej na przykładzie identyfikacji białek
Influence of data description on efficiency of learning and job artificial neural network on example of identification of proteins

Autorzy:

BARTMAN, Jacek

Powiązania:

https://bibliotekanauki.pl/articles/457310.pdf

Data publikacji:

2013

Wydawca:

Uniwersytet Rzeszowski

Tematy:

sztuczna sieć neuronowa
uczenie
artificial neural network
learning

Opis:

Uczenie jednokierunkowych wielowarstwowych sztucznych sieci neuronowych jest zagadnieniem szeroko omawianym w literaturze. Autorzy większości opracowań skupiają się na metodach uczenia, zdecydowanie mniej prac poświęconych jest wpływowi preprocesingu danych na uczenie i efektywność pracy sieci. Skoro uczenie sztucznych sieci neuronowych jest szukaniem funkcji odwzorowującej zbiór danych wejściowych w zbiór oczekiwanych odpowiedzi, to czego możemy oczekiwać, jeżeli zmienimy opis danych uczących? Zmienia się funkcja odwzorowująca, a więc szukamy innej funkcji, zatem jest możliwe, iż sposób kodowania danych wpływa na efektywność uczenia i pracy sieci. Niniejsza praca dotyka przedstawione zagadnienie badając wpływ sposobu zakodowania opisu białek na efektywność uczenia oraz pracy sieci neuronowej identyfikującej rodzaj białka
Learning feedforward multilayer neural networks is an issue widely discussed in the literature. The authors of the most works focus on methods of learning, much less work is devoted to the influence of data preprocessing on learning and the efficiency of the network. If learning of artificial neural networks is finding the mapping function set of input data into a set of expected responses, what you can expect if you change the description of the data learners? Changes of mapping functions, and so we are looking for another function, so it is possible that the encoding of data affects the efficiency of learning and job of the network. This paper touches the issue presented by examining the impact of coding method information about the proteins on the effectiveness of learning and the work of the neural network identifies the type of protein.

Źródło:

Edukacja-Technika-Informatyka; 2013, 4, 2; 358-365
2080-9069

Pojawia się w:

Edukacja-Technika-Informatyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

Detection of the presence of rail corrugation using convolutional neural network

Autorzy:

Tabaszewski, Maciej
Firlik, Bartosz

Powiązania:

https://bibliotekanauki.pl/articles/38890045.pdf

Data publikacji:

2022

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

corrugation
vibration and noise
machine learning
convolutional network

Opis:

Rail corrugation is a significant problem not only in heavy-haul freight but also in light rail systems. Over the last years, considerable progress has been made in understanding, measuring and treating corrugation problems also considered a matter of safety. In the presented research, convolutional neural networks (CNNs) are used to identify the occurrence of rail corrugation in light rail systems. The paper shows that by simultaneously measuring the vibration and the sound pressure, it is possible to identify the rail corrugation with a very small error.

Źródło:

Engineering Transactions; 2022, 70, 4; 339-353
0867-888X

Pojawia się w:

Engineering Transactions

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

Teachers’ learning processes of information competences in the network society – proposed theoretical and methodological solutions

Autorzy:

Perzycka, Elżbieta

Powiązania:

https://bibliotekanauki.pl/articles/2012215.pdf

Data publikacji:

2015-06-30

Wydawca:

Wydawnictwo Adam Marszałek

Tematy:

information literacy
network society
learning process
educational programme

Opis:

Dynamic changes characteristic of modern societies, especially those involved heavily in technological transformation create special conditions for adaptation of the network environment to the needs of education. The introduction to this article discusses contexts of the issue as a precondition resulting from the threat of digital divide of teachers. Then, the paper includes considerations on the conditions of the constitution of information literacy as a result of the revision of theory and practice. It proposes a process of examination of teacher information literacy in the digital environment in the perspective of hermeneutic methodology. Conclusions provide the proposal to include research on the development of teacher information literacy in the process of the network society formation.

Źródło:

The New Educational Review; 2015, 40; 180-188
1732-6729

Pojawia się w:

The New Educational Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

Classification of traffic over collaborative iot/cloud platforms using deep-learning recurrent LSTM

Autorzy:

Patil, Sonali A.
Raj, Arun L.

Powiązania:

https://bibliotekanauki.pl/articles/2097958.pdf

Data publikacji:

2021

Wydawca:

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

Tematy:

IoT
network traffic
machine learning
classification
cloud computing

Opis:

The Internet of Things (IoT) and cloud-based collaborative platforms have emerged as new infrastructures over the recent decades. The classification of network traffic in terms of benign and malevolent traffic is indispensable for IoT/cloud-based collaborative platforms for optimally utilizing channel capac ity for transmitting benign traffic and blocking malicious traffic. The traffic classification mechanism should be dynamic and capable enough for classifying network traffic in a quick manner so that malevolent traffic can be identified at earlier stages and benign traffic can be speedily channelized to the destined nodes. In this paper, we present a deep-learning recurrent LSTM RNet-based technique for classifying traffic over IoT/cloud platforms using the Word2Vec approach. Machine-learning techniques (MLTs) have also been employed for comparing the performance of these techniques with the proposed LSTM RNet classification method. In the proposed research work, network traffic is clas sified into three classes: Tor-Normal, NonTor-Normal, and NonTor-Malicious traffic. The research outcome shows that the proposed LSTM RNet accurately classifies such traffic and also helps reduce network latency as well as enhance data transmission rates and network throughput.

Źródło:

Computer Science; 2021, 22 (3); 367-385
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

PCA-based approximation of a class of distributed parameter systems: classical vs. neural network approach

Autorzy:

Bartecki, K.

Powiązania:

https://bibliotekanauki.pl/articles/201641.pdf

Data publikacji:

2012

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

distributed parameter system
principal component analysis
artificial neural network
supervised learning
unsupervised learning

Opis:

In this article, an approximation of the spatiotemporal response of a distributed parameter system (DPS) with the use of the principal component analysis (PCA) is considered. Based on a data obtained by the numerical solution of a set of partial differential equations, a PCA-based approximation procedure is performed. It consists in the projection of the original data into the subspace spanned by the eigenvectors of the data covariance matrix, corresponding to its highest eigenvalues. The presented approach is carried out using both the classical PCA method as well as two different neural network structures: two-layer feed-forward network with supervised learning (FF-PCA) and single-layer network with unsupervised, generalized Hebbian learning rule (GHA-PCA). In each case considered, the effect of the approximation model structure represented by the number of eigenvectors (or, in the neural case, units in the network projection layer) on the mean square approximation error of the spatiotemporal response and on the data compression ratio is analysed. As shown in the paper, the best approximation quality is obtained for the classical PCA method as well as for the FF-PCA neural approach. On the other hand, an adaptive learning method for the GHA-PCA network allows to use it in e.g. an on-line identification scheme.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 3; 651-660
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: 25.

Tytuł:

Sieci współpracy i samokształcenia nauczycieli – pozory zmiany czy przestrzeń możliwości rozwoju kultury szkoły i jej uczestników

Autorzy:

Ewa, Filipiak,

Powiązania:

https://bibliotekanauki.pl/articles/892384.pdf

Data publikacji:

2019-04-29

Wydawca:

Uniwersytet Warszawski. Wydawnictwa Uniwersytetu Warszawskiego

Tematy:

learning in the zone of proximal development
transformative learning
learning by expanding
networks of learning professionals
collective teacher’s professionalism
strategies and tools for learning about the teacher in the network
collaborative learning in professional communities

Opis:

Setting up cooperation and teachers’ self-education networking  has been determined by a top-down regulation of the Minister of Education. It seems that in the course of implementation activities, legislative and administrative interventions related to this recommendation, one has lost the thinking of the nature and special characteristics of this type of learning and knowledge. The article analyses the special features of the collective learning process, and presents the fundamental theories constituting the interpretive and paradigmatic framework for the learning interpreted in such a way: Lev S.Vygotski’s cultural-historical theory, Jerome S. Bruner’s socio-cultural theory, Yrjö Engeström’s expansive learning theory and learning by expanding, Jack Mezirow’s  transformative learning, Etienne Wenger’s situated learning theory and Jean Lave and Etienne Wenger’s community of practice concept, a participant of “teaching conversation”, the specific tools and strategies necessary to equip the cognitive box with teachers’ tools, have been selected and characterised. An example of a network of learning professionals  is shown. In conclusion, one highlights the apparent activities of the created networks, projecting a certain understanding and instrumental understanding of the practice on practitioners, which hinders Bruner’’s challenge of transforming the school into a culture of learning communities.

Źródło:

Kwartalnik Pedagogiczny; 2019, 64(1(251)); 33-46
0023-5938

Pojawia się w:

Kwartalnik Pedagogiczny

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

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: 27.

Tytuł:

Report on the Implementation of WorkPackage 2 “Analyses of Legal, Ethical, Human, Technical and Social Factors of ICT and E-Learning Development and Intercultural Competences State in Every Partner Country” in the Framework of the IRNet Project

Autorzy:

Smyrnova-Trybulska, Eugenia
Ogrodzka-Mazur, Ewa
Szafrańska-Gajdzica, Anna
Doluk, Ewelina
Kommers, Piet
Morze, Nataliia
Noskova, Tatiana
Pavlova, Tatiana
Yakovleva, Olga
Pinto, Paulo
Arias Masa, Juan
Cubo Delgado, Sixto
Delicado Puerto, Gemma
Drlík, Martin
Malach, Josef
Issa, Tomayess
Romaniukha, Maryna

Powiązania:

https://bibliotekanauki.pl/articles/1366003.pdf

Data publikacji:

2016-12-01

Wydawca:

Wydawnictwo Uniwersytetu Śląskiego

Tematy:

International Research Network IRNet
ICT
e-learning
intercultural competences

Opis:

This article, prepared by an international team of researchers from different scientific areas, connected with ICT, e-learning, pedagogy, and other related disciplines, focuses on the objectives and some results of the international project IRNet. In particular, the article describes research tools, methods, and a procedure of the WP2, that is, analyses of legal, ethical, human, technic al, and social factors of ICT and e-learning development, and the state of intercultural competences in partner countries: objectives, tasks, deliverables, and implementation of research trips. Researchers from Poland, the Netherlands, Spain, Slovakia, Portugal, Czech Republic, Australia, Ukraine, and Russia analyzed the results of WP2 in the context of the next stages and Work packages of IRNet project – International Research Network.

Źródło:

International Journal of Research in E-learning IJREL; 2015, 1, 1; 99-116
2451-2583
2543-6155

Pojawia się w:

International Journal of Research in E-learning IJREL

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

On Efficiency of Selected Machine Learning Algorithms for Intrusion Detection in Software Defined Networks

Autorzy:

Jankowski, D.
Amanowicz, M.

Powiązania:

https://bibliotekanauki.pl/articles/963945.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

software defined network
intrusion detection
machine learning
Mininet
SDN

Opis:

We propose a concept of using Software Defined Network (SDN) technology and machine learning algorithms for monitoring and detection of malicious activities in the SDN data plane. The statistics and features of network traffic are generated by the native mechanisms of SDN technology.In order to conduct tests and a verification of the concept, it was necessary to obtain a set of network workload test data.We present virtual environment which enables generation of the SDN network traffic.The article examines the efficiency of selected machine learning methods: Self Organizing Maps and Learning Vector Quantization and their enhanced versions.The results are compared with other SDN-based IDS.

Źródło:

International Journal of Electronics and Telecommunications; 2016, 62, 3; 247-252
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Combining fuzzy and cellular learning automata methods for clustering wireless sensor network to increase life of the network

Autorzy:

Aramideh, J
Jelodar, H

Powiązania:

https://bibliotekanauki.pl/articles/957968.pdf

Data publikacji:

2014

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

wireless sensor network
clustering
fuzzy logic
cellular learning automata

Opis:

Wireless sensor networks have attracted attention of researchers considering their abundant applications. One of the important issues in this network is limitation of energy consumption which is directly related to life of the network. One of the main works which have been done recently to confront with this problem is clustering. In this paper, an attempt has been made to present clustering method which performs clustering in two stages. In the first stage, it specifies candidate nodes for being head cluster with fuzzy method and in the next stage, the node of the head cluster is determined among the candidate nodes with cellular learning automata. Advantage of the clustering method is that clustering has been done based on three main parameters of the number of neighbors, energy level of nodes and distance between each node and sink node which results in selection of the best nodes as a candidate head of cluster nodes. Connectivity of network is also evaluated in the second part of head cluster determination. Therefore, more energy will be stored by determining suitable head clusters and creating balanced clusters in the network and consequently, life of the network increases.

Źródło:

Advances in Science and Technology. Research Journal; 2014, 8, 24; 1-8
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

COLLABORATIVE ACADEMIC PROJECTS ON SOCIAL NETWORK SITES TO SOCIALIZE EAP STUDENTS INTO ACADEMIC COMMUNITIES OF PRACTICE

Autorzy:

Dashtestani, Reza

Powiązania:

https://bibliotekanauki.pl/articles/955834.pdf

Data publikacji:

2018

Wydawca:

Uniwersytet Marii Curie-Skłodowskiej w Lublinie. IATEFL Poland Computer Special Interest Group

Tematy:

collaborative learning
social network sites
English for Academic Purposes

Opis:

Learning English for academic purposes (EAP) can help university students promote their academic literacy through socializing them into academic communities of practice. This study examined the impact of the use of collaborative projects on three social network sites on EAP students’ attitudes towards EAP and academic content learning. Three groups of students from three disciplines, i.e. engineering (n = 54), social sciences (n = 57), and basic sciences (n = 62) participated in the study. The students participated in collaborative projects on three social network sites, i.e. Facebook, LinkedIn, and ResearchGate, for a period of four months with the help of their teachers. Questionnaires and semi-structured interviews were utilized as the instruments of the study. The results suggested that the students from the three disciplines had positive attitudes towards carrying out collaborative projects on three social network sites. No significant difference was identified regarding students’ attitudes. The perceived benefits of the project work included opportunities for having international communication, learning academic vocabulary, peer collaboration, teacher support, and opportunities for improving academic English and academic literacy. The study further explored students’ attitudes towards factors which affected students’ project work and the limitations of the use of collaborative projects on three social network sites. The students showed a preference for using Facebook; however they did not agree on their interest in the use of ResearchGate and LinkedIn. The findings can have implications for integrating the three social network sites in EAP instruction.

Źródło:

Teaching English with Technology; 2018, 18, 2; 3-20
1642-1027

Pojawia się w:

Teaching English with Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Identification method for power quality disturbances in distribution network based on transfer learning

Autorzy:

Heping, Peng
Wenxiong, Mo
Yong, Wang
Le, Luan
Zhong, Xu

Powiązania:

https://bibliotekanauki.pl/articles/2135730.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

disturbance identification
distribution network
multiple transfer learning
power quality

Opis:

For a higher classification accuracy of disturbance signals of power quality, a disturbance classification method for power quality based on gram angle field and multiple transfer learning is proposed in this paper. Firstly, the one-dimensional disturbance signal of power quality is transformed into a Gramian angular field (GAF) coded image by using the gram angle field, and then three ResNet networks are constructed. The disturbance signals with representative signal-to-noise ratios of 0 dB, 20 dB and 40 dB are selected as the input of the sub-model to train the three sub-models, respectively. During this period, the training weights of the sub-models are transferred in turn by using the method of multiple transfer learning. The pre-training weight of the latter model is inherited from the training weight of the previous model, and the weight processing methods of partial freezing and partial fine-tuning are adopted to ensure the optimal training effect of the model. Finally, the features of the three sub-models are fused to train the classifier with a full connection layer, and a disturbance classification model for power quality is obtained. The simulation results show that the method has higher classification accuracy and better anti-noise performance, and the proposed model has good robustness and generalization.

Źródło:

Archives of Electrical Engineering; 2022, 71, 3; 731--754
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

Research on the risk classification of cruise ship fires based on an attention-BP neural network

Autorzy:

Xiong, Zhenghua
Xiang, Bo
Chen, Ye
Chen, Bin

Powiązania:

https://bibliotekanauki.pl/articles/32912853.pdf

Data publikacji:

2022

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

cruise fire
simulation modeling
ensemble learning
BP neural network

Opis:

Due to the relatively closed environment, complex internal structure, and difficult evacuation of personnel, it is more difficult to prevent ship fires than land fires. In this paper, taking the large cruise ship as the research object, the physical model of a cruise cabin fire is established through PyroSim software, and the safety indexes such as smoke temperature, CO concentration, and visibility are numerically simulated. An Attention-BP neural network model is designed for realizing the intelligent identification of a cabin fire and dividing the risk level, which integrates the diagnosis results of multiple neural network models through the self-Attention mechanism and adaptively distributes the weight of each BP neural network model. The proposed model can provide decision-making reference for subsequent fire-fighting measures and personnel evacuation. Experimental results show that the proposed Attention-BP neural network model can effectively realize the early warning of the fire risk level. Compared with other machine learning algorithms, it has the highest stability and accuracy and reduces the uncertainty of early cabin fire warning.

Źródło:

Polish Maritime Research; 2022, 3; 61-68
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

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: 34.

Tytuł:

Hybrid, finite element-artificial neural network model for composite materials
Zastosowanie sztucznych sieci neuronowych w modelowaniu numerycznym kompozytów przy pomocy metody elementów skończonych

Autorzy:

Lefik, M.

Powiązania:

https://bibliotekanauki.pl/articles/281993.pdf

Data publikacji:

2004

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

artificial neural network
composite materials
self-learning FE method

Opis:

An application of Artificial Neural Networks for a definition of the effective constitutive law for a composite is described in the paper. First, a classical homogenisation procedure is directly interpreted with a use of this numerical tool. Next, a self-learning Finite Element code (FE with ANN inside) is used in the case when the effective constitutive law is deduced from a numerical experiment (substituting here a purely phenomenological approach). The new contribution to the classical self-learning procedure consists of its adaptation to a case of a non-monotonic loading (non-to-one load-deformation curve). This new ability of the method is principally due to the incremental form of the constitutive equation and the respective scheme of the neural network structure. Also an organisation of a constitutive data-base containing learning patterns is suitably modified. It is shown by examples that the training process is very quick. The error of this method is smaller, comparing to other schemes of data acquisition.
W artykule opisano zastosowanie sztucznych sieci neuronowych do określenia efektywnego związku konstytutywnego dla kompozytów. To narzędzie numeryczne użyte zostało dwojako: do bezpośredniego zapisu wyników otrzymanych w ramach klasycznej metody homogenizacji oraz do wnioskowania o własnościach efektywnych na podstawie eksperymentu numerycznego (zastępującego eksperyment rzeczywisty) wykonanego na małej, lecz reprezentatywnej próbce kompozytu. W tym drugim przypadku zastosowano schemat "samouczącego się" programu metody elementów skończonych, w którym związek konstytutywny opisany jest siecią neuronową. Schemat ten zaadaptowano tak, że może być użyty w przypadku obciążeń niemonotonicznych oraz wtedy, gdy zależność: miara odkształcenia-miara naprężenia nie jest wzajemnie jednoznaczna. Te nowe możliwości uzyskane zostały dzięki przedstawieniu związku konstytutywnego w formie przyrostowej oraz opracowania odpowiedniej do tego budowy sieci neuronowej. Schemat "samouczącego się" programu MES charakteryzuje się tym, że proces formułowania nieznanego związku konstytutywnego jest szybki, a zgodność modelu numerycznego z eksperymentem większa niż dla innych metod.

Źródło:

Journal of Theoretical and Applied Mechanics; 2004, 42, 3; 539-563
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

Bayesian Network Modeling in Discovering Risk Factors of Dental Caries in Three-Year-Old Children

Autorzy:

Łaguna, W.
Bagińska, J.
Oniśko, A.

Powiązania:

https://bibliotekanauki.pl/articles/1918880.pdf

Data publikacji:

2019-08-26

Wydawca:

Uniwersytet Medyczny w Białymstoku

Tematy:

dental caries
Bayesian network
learning from data
risk assessment

Opis:

Purpose - The aim of this study was to use probabilistic graphical models to determine dental caries risk factors in three-year-old children. The analysis was conducted on the basis of the questionnaire data and resulted in building probabilistic graphical models to investigate dependencies among the features gathered in the surveys on dental caries. Materials and Methods - The data available in this analysis came from dental examinations conducted in children and from a questionnaire survey of their parents or guardians. The data represented 255 children aged between 36 and 48 months. Self-administered questionnaires contained 34 questions of socioeconomic and medical nature such as nutritional habits, wealth, or the level of education. The data included also the results of oral examination by a dentist. We applied the Bayesian network modeling to construct a model by learning it from the collected data. The process of Bayesian network model building was assisted by a dental expert. Results - The model allows to identify probabilistic relationships among the variables and to indicate the most significant risk factors of dental caries in three-year-old children. The Bayesian network model analysis illustrates that cleaning teeth and falling asleep with a bottle are the most significant risk factors of dental caries development in three-year-old children, whereas socioeconomic factors have no significant impact on the condition of teeth. Conclusions - Our analysis results suggest that dietary and oral hygiene habits have the most significant impact on the occurrence of dental caries in three-year-olds.

Źródło:

Progress in Health Sciences; 2019, 1; 118-125
2083-1617

Pojawia się w:

Progress in Health Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Predictive modelling of turbofan engine components condition using machine and deep learning methods

Autorzy:

Matuszczak, Michał
Żbikowski, Mateusz
Teodorczyk, Andrzej

Powiązania:

https://bibliotekanauki.pl/articles/1841686.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

reliability
prognostics
deep learning
machine learning
gas turbine
turbofan engine
neural network
condition-based maintenance

Opis:

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

Źródło:

Eksploatacja i Niezawodność; 2021, 23, 2; 359-370
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

Artificial neural network (ANN) modelling to estimate bubble size from macroscopic image and object features

Autorzy:

Vinnett, Luis
León, Roberto
Mesa, Diego

Powiązania:

https://bibliotekanauki.pl/articles/29552038.pdf

Data publikacji:

2023

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

machine learning
artificial neural network
flotation
bubble size
Sauter diameter

Opis:

Bubble size measurements in aerated systems such as froth flotation cells are critical for controlling gas dispersion. Commonly, bubbles are measured by obtaining representative photographs, which are then analyzed using segmentation and identification software tools. Recent developments have focused on enhancing these segmentation tools. However, the main challenges around complex bubble cluster segmentation remain unresolved, while the tools to tackle these challenges have become increasingly complex and computationally expensive. In this work, we propose an alternative solution, circumventing the need for image segmentation and bubble identification. An Artificial Neural Network (ANN) was trained to estimate the Sauter mean bubble size (D32) based on macroscopic image features obtained with simple and inexpensive image analysis. The results showed excellent prediction accuracy, with a correlation coefficient, R, over 0.998 in the testing stage, and without bias in its error distribution. This machine learning tool paves the way for robust and fast estimation of bubble size under complex bubble images, without the need of image segmentation.

Źródło:

Physicochemical Problems of Mineral Processing; 2023, 59, 5; art. no. 185759
1643-1049
2084-4735

Pojawia się w:

Physicochemical Problems of Mineral Processing

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Report on the Implementation of Work Package 5 “Pilot Methodology Development” in the Framework of the IRNet Project

Autorzy:

Smyrnova-Trybulska, Eugenia
Morze, Nataliia
Kommers, Piet
Noskova, Tatiana
Pinto, Paulo
Cubo Delgado, Sixto
Drlík, Martin
Malach, Josef
Issa, Tomayess
Romanyukha, Maryna

Powiązania:

https://bibliotekanauki.pl/articles/448377.pdf

Data publikacji:

2017-07-10

Wydawca:

Wydawnictwo Uniwersytetu Śląskiego

Tematy:

International Research Network IRNet
ICT
e-learning
intercultural competencies
methodology

Opis:

This article, prepared by an international team of researchers from different scientific areas connected with ICT, e-learning, pedagogy, and other related disciplines, focuses on the objectives and some results of the international project IRNet. In particular, the article describes research tools, methods, and a procedure of the Work Package 5 (WP5), that is, objectives, tasks, deliverables, and implementation of research trips in the context of the next stages and Work Packages of IRNet project – International Research Network.

Źródło:

International Journal of Research in E-learning IJREL; 2017, 3, 1; 127-159
2451-2583
2543-6155

Pojawia się w:

International Journal of Research in E-learning IJREL

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

Report on the Implementation of Work Package 6 “Implementation of Methodology” in the Framework of the IRNet Project

Autorzy:

Smyrnova-Trybulska, Eugenia
Malach, Josef
Kostolányová, Kateřina
Morze, Nataliia
Kommers, Piet
Noskova, Tatiana
Pinto, Paulo
Cubo Delgado, Sixto
Drlík, Martin
Issa, Tomayess
Romanyukha, Maryna

Powiązania:

https://bibliotekanauki.pl/articles/448406.pdf

Data publikacji:

2017-11-10

Wydawca:

Wydawnictwo Uniwersytetu Śląskiego

Tematy:

International Research Network IRNet
methodology
ICT
e-learning
intercultural competences

Opis:

This article, prepared by an international team of researchers from different scientific areas connected with ICT, e-learning, pedagogy, and other related disciplines, focuses on the objectives and some results of the international project IRNet (www.irnet.us.edu.pl). In particular, the article describes research tools, methods, and a procedure of the Work Package 6 “Implementation of Methodology,” that is, objectives, tasks, deliverables, publications, and implementation of research trips in the context of the next stages and Work Packages of IRNet project – International Research Network.

Źródło:

International Journal of Research in E-learning IJREL; 2017, 3, 2; 93-119
2451-2583
2543-6155

Pojawia się w:

International Journal of Research in E-learning IJREL

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

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: 41.

Tytuł:

Atrial fibrillation detection on electrocardiograms with convolutional neural networks
Detekcja migotania przedsionków na elektrokardiogramach z wykorzystaniem konwolucyjnej sieci neuronowej

Autorzy:

Kifer, Viktor
Zagorodna, Natalia
Hevko, Olena

Powiązania:

https://bibliotekanauki.pl/articles/408581.pdf

Data publikacji:

2019

Wydawca:

Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej

Tematy:

electrocardiography
machine learning
neural network
elektrokardiografia
nauczanie maszynowe
sieć neuronowa

Opis:

In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.
W tej pracy, przedstawiamy nasze badania, które potwierdzają przydatność zastosowania konwolucyjnych sieci neuronowych dla klasyfikacji zapisów jedno-odprowadzeniowego EKG. (tak brzmi ta nazwa). Proponowana metoda została zaprojektowana dla klasyfikowania prawidłowego rytmu zatokowego, migotania przedsionków (AF), poza-AF powiązanych z innymi nieprawidłowymi rytmami serca i zaszumionymi (głośnymi?) sygnałami. Ta metoda łączy cechy wyselekcjonowane ręcznie z cechami wyuczonymi przez głębokie sieci neuronowe. Zbiór danych Physionet Challenge 2017 zawierający ponad 8500 zapisów EKG został zastosowany dla modelu szkolenia oraz walidacji. Model nauczony (wyszkolony?) osiąga odpowiednio średni F1-wynik 0.71 w klasyfikowaniu prawidłowego rytmu zatokowego, rytmu AF oraz innych rytmów.

Źródło:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2019, 9, 4; 69-73
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: 42.

Tytuł:

Detection of Monocrystalline Silicon Wafer Defects Using Deep Transfer Learning

Autorzy:

Ganum, Adriana
Iskandar, D. N. F. Awang
Chin, Lim Phei
Fauzi, Ahmad Hadinata

Powiązania:

https://bibliotekanauki.pl/articles/2058502.pdf

Data publikacji:

2022

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

automated optical inspection
machine learning
neural network
wafer imperfection identification

Opis:

Defect detection is an important step in industrial production of monocrystalline silicon. Through the study of deep learning, this work proposes a framework for classifying monocrystalline silicon wafer defects using deep transfer learning (DTL). An existing pre-trained deep learning model was used as the starting point for building a new model. We studied the use of DTL and the potential adaptation of Mo bileNetV2 that was pre-trained using ImageNet for extracting monocrystalline silicon wafer defect features. This has led to speeding up the training process and to improving performance of the DTL-MobileNetV2 model in detecting and classifying six types of monocrystalline silicon wafer defects (crack, double contrast, hole, microcrack, saw-mark and stain). The process of training the DTL-MobileNetV2 model was optimized by relying on the dense block layer and global average pooling (GAP) method which had accelerated the convergence rate and improved generalization of the classification network. The monocrystalline silicon wafer defect classification technique relying on the DTL-MobileNetV2 model achieved the accuracy rate of 98.99% when evaluated against the testing set. This shows that DTL is an effective way of detecting different types of defects in monocrystalline silicon wafers, thus being suitable for minimizing misclassification and maximizing the overall production capacities.

Źródło:

Journal of Telecommunications and Information Technology; 2022, 1; 34--42
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: 43.

Tytuł:

Performance analysis of multi-layered clustering network using fault tolerance multipath routing protocol (MRP-FT) in a wireless sensor network (WSN)

Autorzy:

Kaur, Gagandeep

Powiązania:

https://bibliotekanauki.pl/articles/2204100.pdf

Data publikacji:

2023

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

scalability
fault tolerance
neural networks
Boltzmann learning
wireless sensor network

Opis:

Wireless sensor networks (WSNs) are ad hoc and self-configuring networks having the possibility that any sensor node can connect or leave the network. With no central controller in WSN, wireless sensor nodes are considered responsible for data routing in the networks. The wireless sensor nodes are very small in size and have limited resources, therefore, it becomes difficult to recharge or replace the battery of the sensor nodes at far places. The present study focused on reducing the battery consumption of the sensor nodes by the deployment of the newly proposed Fault Tolerance Multipath Routing Protocol (MRP-FT) as compared with the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol under particle swarm optimisation based fault tolerant routing (PSO-FT) technique. The proposed algorithm of MRP-FT-based on the dynamic clustering technique using Boltzmann learning of the neural network and the weights were adjusted according to the area of networks, number of nodes and rounds, the initial energy of nodes (E0), transmission energy of nodes (d

Źródło:

Operations Research and Decisions; 2023, 33, 1; 75--92
2081-8858
2391-6060

Pojawia się w:

Operations Research and Decisions

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

An investigation of the relationship between encoder difference and thermo-elastic machine tool deformation

Autorzy:

Brecher, Christian
Dehn, Mathias
Neus, Stephan

Powiązania:

https://bibliotekanauki.pl/articles/24084708.pdf

Data publikacji:

2023

Wydawca:

Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych

Tematy:

machine tool
thermal error compensation
machine learning
artificial neural network

Opis:

New approaches, using machine learning to model the thermo-elastic machine tool error, often rely on machine internal data, like axis speed or axis position as input data, which have a delayed relation to the thermo-elastic error. Since there is no direct relation to the thermo-elastic error, this can lead to an increased computation inaccuracy of the model or the need for expensive sensor equipment for additional input data. The encoder difference is easy to obtain and has a direct relationship with the thermo-elastic error and therefore has a high potential to improve the accuracy thermo-elastic error models. This paper first investigates causes of the encoder difference and its relationship with the thermo-elastic error. Afterwards, the model is presented, which uses the encoder difference to compute the thermo-elastic error. Due to the complexity of the relationship, it is necessary, to use a machine learning approach for this. To conclude, the potential of the encoder difference as an input of the model is evaluated.

Źródło:

Journal of Machine Engineering; 2023, 23, 3; 26--37
1895-7595
2391-8071

Pojawia się w:

Journal of Machine Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

QIBMRMN: Design of a Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks

Autorzy:

Doorwar, Minaxi
Malathi, P

Powiązania:

https://bibliotekanauki.pl/articles/27311958.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

multimedia
network
Q-learning
GWO
GA
Adhoc
QoS
iterative
process

Opis:

Multimedia networks utilize low-power scalar nodes to modify wakeup cycles of high-performance multimedia nodes, which assists in optimizing the power-toperformance ratios. A wide variety of machine learning models are proposed by researchers to perform this task, and most of them are either highly complex, or showcase low-levels of efficiency when applied to large-scale networks. To overcome these issues, this text proposes design of a Q-learning based iterative sleep-scheduling and fuses these schedules with an efficient hybrid bioinspired multipath routing model for largescale multimedia network sets. The proposed model initially uses an iterative Q-Learning technique that analyzes energy consumption patterns of nodes, and incrementally modifies their sleep schedules. These sleep schedules are used by scalar nodes to efficiently wakeup multimedia nodes during adhoc communication requests. These communication requests are processed by a combination of Grey Wolf Optimizer (GWO) & Genetic Algorithm (GA) models, which assist in the identification of optimal paths. These paths are estimated via combined analysis of temporal throughput & packet delivery performance, with node-to-node distance & residual energy metrics. The GWO Model uses instantaneous node & network parameters, while the GA Model analyzes temporal metrics in order to identify optimal routing paths. Both these path sets are fused together via the Q-Learning mechanism, which assists in Iterative Adhoc Path Correction (IAPC), thereby improving the energy efficiency, while reducing communication delay via multipath analysis. Due to a fusion of these models, the proposed Q-Learning based Iterative sleep-scheduling & hybrid Bioinspired Multipath Routing model for Multimedia Networks (QIBMRMN) is able to reduce communication delay by 2.6%, reduce energy consumed during these communications by 14.0%, while improving throughput by 19.6% & packet delivery performance by 8.3% when compared with standard multimedia routing techniques.

Źródło:

International Journal of Electronics and Telecommunications; 2023, 69, 4; 776--784
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Designing a modern cloud-oriented virtual personalized educational environment

Autorzy:

Morze, Nataliia
Spivak, Svitlana
Smyrnova-Trybulska, Eugenia

Powiązania:

https://bibliotekanauki.pl/articles/2012126.pdf

Data publikacji:

2015-06-30

Wydawca:

Wydawnictwo Adam Marszałek

Tematy:

personal e-learning environment
cloud computing
network services
distance courses
formal
informal and non-formal learning

Opis:

This paper focuses on students’ research ability to use information and communication technologies to carry out information activities in their professional field. The results of studies on personalized and adaptive learning, based on the consideration of learning styles were analyzed. Based on the statistical analysis of the pedagogical experiments some recommendations were formulated for technology training for teachers and students, to improve efficiency training.

Źródło:

The New Educational Review; 2015, 40; 140-154
1732-6729

Pojawia się w:

The New Educational Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

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: 48.

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: 49.

Tytuł:

Artificial intelligence applications in project scheduling: a systematic review, bibliometric analysis, and prospects for future research

Autorzy:

Bahroun, Zied
Tanash, Moayad
Ad, Rami As
Alnajar, Mohamad

Powiązania:

https://bibliotekanauki.pl/articles/27315576.pdf

Data publikacji:

2023

Wydawca:

STE GROUP

Tematy:

artificial intelligence
machine learning
project scheduling
bibliometric analysis
network analysis
review

Opis:

The availability of digital infrastructures and the fast-paced development of accompanying revolutionary technologies have triggered an unprecedented reliance on Artificial intelligence (AI) techniques both in theory and practice. Within the AI domain, Machine Learning (ML) techniques stand out as essential facilitator largely enabling machines to possess human-like cognitive and decision making capabilities. This paper provides a focused review of the literature addressing applications of emerging ML toolsto solve various Project Scheduling Problems (PSPs). In particular, it employs bibliometric and network analysis tools along with a systematic literature review to analyze a pool of 104 papers published between 1985 and August 2021. The conducted analysis unveiled the top contributing authors, the most influential papers as well as the existing research tendencies and thematic research topics within this field of study. A noticeable growth in the number of relevant studies is seen recently with a steady increase as of the year 2018. Most of the studies adopted Artificial Neural Networks, Bayesian Network and Reinforcement Learning techniques to tackle PSPs under a stochastic environment, where these techniques are frequently hybridized with classical metaheuristics. The majority of works (57%) addressed basic Resource Constrained PSPs and only 15% are devoted to the project portfolio management problem. Furthermore, this study clearly indicates that the application of AI techniques to efficiently handle PSPs is still in its infancy stage bringing out the need for further research in this area. This work also identifies current research gaps and highlights a multitude of promising avenues for future research.

Źródło:

Management Systems in Production Engineering; 2023, 2 (31); 144--161
2299-0461

Pojawia się w:

Management Systems in Production Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 50.

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: 51.

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: 52.

Tytuł:

Basic concepts of dynamic recurrent neural networks development

Autorzy:

Boyko, N.
Pobereyko, P.

Powiązania:

https://bibliotekanauki.pl/articles/410971.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Oddział w Lublinie PAN

Tematy:

recurrent neural network
dynamic system
learning algorithms
reservoir computing
unsteady dynamics

Opis:

In this work formulated relevance, set out an analytical review of existing approaches to the research recurrent neural networks (RNN) and defined precondition appearance a new direction in the field neuroinformatics – reservoir computing. Shows generalized classification neural network (NN) and briefly described main types dynamics and modes RNN. Described topology, structure and features of the model NN with different nonlinear functions and with possible areas of progress. Characterized and systematized wellknown learning methods RNN and conducted their classification by categories. Determined the place RNN with unsteady dynamics of other classes RNN. Deals with the main parameters and terminology, which used to describe models RNN. Briefly described practical implementation recurrent neural networks in different areas natural sciences and humanities, and outlines and systematized main deficiencies and the advantages of using different RNN. The systematization of known recurrent neural networks and methods of their study is performed and on this basis the generalized classification of neural networks was proposed.

Źródło:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2016, 5, 2; 63-68
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: 53.

Tytuł:

ADOPTING LEARNING DESIGN WITH LAMS: MULTI- DIMENSIONAL, SYNCHRONOUS LARGE-SCALE ADOPTION OF INNOVATION

Autorzy:

Badilescu-Buga, Emil

Powiązania:

https://bibliotekanauki.pl/articles/941240.pdf

Data publikacji:

2012

Wydawca:

Uniwersytet Marii Curie-Skłodowskiej w Lublinie. IATEFL Poland Computer Special Interest Group

Tematy:

learning design
LAMS
adoption life cycle
social network
information cognitive structures

Opis:

Learning Activity Management System (LAMS) has been trialled and used by users from many countries around the globe, but despite the positive attitude towards its potential benefits to pedagogical processes its adoption in practice has been uneven, reflecting how difficult it is to make a new technology based concept an integral part of the education system. In order to investigate and determine the elements that block the adoption of learning design tools in general, the study will review research papers that have been published in recent years on this subject, especially LAMS. The study will discuss patterns of critical aspects related to adoption of learning design tools and derive a framework that can be used in follow-up studies aimed at collecting relevant empirical data from practitioners to identify key progress measures of the adoption process. These measures may be used later to devise strategies that will see increased adoption of online learning design tools such as LAMS in school systems and higher education institutions.

Źródło:

Teaching English with Technology; 2012, 12, 2; 18-35
1642-1027

Pojawia się w:

Teaching English with Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 54.

Tytuł:

Subpixel localization of optical vortices using artificial neural networks

Autorzy:

Popiołek-Masajada, Agnieszka
Frączek, Ewa
Burnecka, Emilia

Powiązania:

https://bibliotekanauki.pl/articles/1849005.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

optical vortex
spiral phase map
pseudo phase
deep learning
neural network

Opis:

Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.

Źródło:

Metrology and Measurement Systems; 2021, 28, 3; 497-508
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 55.

Tytuł:

Subpixel localization of optical vortices using artificial neural networks

Autorzy:

Popiołek-Masajada, Agnieszka
Frączek, Ewa
Burnecka, Emilia

Powiązania:

https://bibliotekanauki.pl/articles/1849096.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

optical vortex
spiral phase map
pseudo phase
deep learning
neural network

Opis:

Optical vortices are getting attention in modern optical metrology. Because of their unique features, they can be used as precise position markers. In this paper, we show that an artificial neural network can be used to improve vortex localization. A deep neural network with several hidden layers was trained to find subpixel vortex positions on the spiral phase maps. Several thousand training samples, differing by spiral density, its orientation, and vortex position, were generated numerically for teaching purposes. As a result, Best Validation Performance of the order of 10-5 pixel has been reached. To verify the usefulness of the proposed method, a related experiment in the setup of an optical vortex scanning microscope has been reported. It is shown that the vortex can be localized with subpixel accuracy also on experimental phase maps.

Źródło:

Metrology and Measurement Systems; 2021, 28, 3; 497-508
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 56.

Tytuł:

An attempt at applying machine learning in diagnosing marine ship engine turbochargers

Autorzy:

Adamkiewicz, Andrzej
Nikończuk, Piotr

Powiązania:

https://bibliotekanauki.pl/articles/2200936.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

machine learning
compressor diagnosis
marine ship engine
operational decision
neural
network

Opis:

The article presents a diagnosis of turbochargers in the supercharging systems of marine engines in terms of maintenance decisions. The efficiency of turbocharger rotating machines was defined. The operating parameters of turbocharging systems used to monitor the correct operation and diagnose turbochargers were identified. A parametric diagnostic test was performed. Relationships between parameters for use in machine learning were selected. Their credibility was confirmed by the results of the parametric test of the turbocharger system and the main engine, verified by the coefficient of determination. A particularly good fit of the describing functions was confirmed. As determinants of the technical condition of a turbocharger, the relationship between the rotational speed of the engine shaft, the turbocharger rotor assembly and the charging air pressure was assumed. In the process of machine learning, relationships were created between the rotational speed of the engine shaft and the boost pressure, and the indicator of the need for maintenance. The accuracy of the maintenance decisions was confirmed by trends in changes in the efficiency of compressors.

Źródło:

Eksploatacja i Niezawodność; 2022, 24, 4; 795--804
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 57.

Tytuł:

Lexicon and attention based handwritten text recognition system

Autorzy:

Kumari, Lalita
Singh, Sukhdeep
Rathore, Vaibhav Varish Singh
Sharma, Anuj

Powiązania:

https://bibliotekanauki.pl/articles/2201262.pdf

Data publikacji:

2022

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Instytut Informatyki Technicznej

Tematy:

handwriting recognition
deep learning
word beam search
attention
neural network
lexicon

Opis:

The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of neural machine translations and automatic speech recognition and now is being implemented in text recognition domain. In this study, we are able to achieve 4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07% character error rate and 16.14% word error rate on GW dataset after merging the attention and word beam search decoder with existing Flor et al. architecture. To analyse further, we have also used system similar to Shi et al. neural network system with greedy decoder and observed 23.27% improvement in character error rate from the base model.

Źródło:

Machine Graphics & Vision; 2022, 31, 1/4; 75--92
1230-0535
2720-250X

Pojawia się w:

Machine Graphics & Vision

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 58.

Tytuł:

An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study
Wykorzystanie heterogenicznych grafowych sieci izomorficznych w analizie danych związanych z praniem brudnych pieniędzy. Studium przypadku FinCEN

Autorzy:

Wójcik, Filip

Powiązania:

https://bibliotekanauki.pl/articles/38890419.pdf

Data publikacji:

2024

Wydawca:

Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu

Tematy:

money laundering
deep learning
machine learning
network analysis
graphs
pranie brudnych pieniędzy
uczenie głębokie
analiza sieci
grafy

Opis:

Aim: This study aimed to develop and apply the novel HexGIN (Heterogeneous extension for Graph Isomorphism Network) model to the FinCEN Files case data and compare its performance with existing solutions, such as the SAGE-based graph neural network and Multi-Layer Perceptron (MLP), to demonstrate its potential advantages in the field of anti-money laundering systems (AML). Methodology: The research employed the FinCEN Files case data to develop and apply the HexGIN model in a beneficiary prediction task for a suspicious transactions graph. The model's performance was compared with the existing solutions in a series of cross-validation experiments. Results: The experimental results on the cross-validation data and test dataset indicate the potential advantages of HexGIN over the existing solutions, such as MLP and Graph SAGE. The proposed model outperformed other algorithms in terms of F1 score, precision, and ROC AUC in both training and testing phases. Implications and recommendations: The findings demonstrate the potential of heterogeneous graph neural networks and their highly expressive architectures, such as GIN, in AML. Further research is needed, in particular to combine the proposed model with other existing algorithms and test the solution on various money-laundering datasets. Originality/value: Unlike many AML studies that rely on synthetic or undisclosed data sources, this research was based on a publicly available, real, heterogeneous transaction dataset, being part of a larger investigation. The results indicate a promising direction for the development of modern hybrid AML tools for analysing suspicious transactions; based on heterogeneous graph networks capable of handling various types of entities and their connections.
Cel: Celem niniejszej analizy jest opracowanie i zastosowanie nowego modelu HexGIN (heterogeniczne rozszerzenie dla izomorfizmu sieci grafowych) do danych z dochodzenia dziennikarskiego FinCEN oraz porównanie jego jakości predykcji z istniejącymi rozwiązaniami, takimi jak sieć SAGE i wielowarstwowa sieć neuronowa (MLP). Metodyka: W badaniach wykorzystano dane ze śledztwa FinCEN do opracowania i zastosowania modelu HexGIN w zadaniu przewidywania beneficjenta sieci powiązanych transakcji finansowych. Skuteczność modelu porównano z istniejącymi rozwiązaniami wykorzystującymi sieci neuronowe grafu w serii eksperymentów z walidacją krzyżową. Wyniki: Eksperymentalne wyniki na danych walidacji krzyżowej i zestawie testowym potwierdzają potencjalne zalety HexGIN w porównaniu z istniejącymi rozwiązaniami, takimi jak MLP i SAGE. Proponowany model przewyższa inne algorytmy pod względem wyniku miary F1, precyzji i ROC AUC, w fazie zarówno treningowej, jak i testowej. Implikacje i rekomendacje: Wyniki pokazują potencjał heterogenicznych grafowych sieci i ich wysoce ekspresyjnych implementacji, takich jak GIN, w analizie transakcji finansowych. Potrzebne są dalsze badania, zwłaszcza w celu połączenia proponowanego modelu z innymi istniejącymi algorytmami i przetestowania rozwiązania na różnych zestawach danych dotyczących problemu prania brudnych pieniędzy. Oryginalność/wartość: W przeciwieństwie do wielu badań, które opierają się na syntetycznych lub nieujawnionych źródłach danych związanych z praniem brudnych pieniędzy, to studium przypadku opiera się na publicznie dostępnych, rzeczywistych, heterogenicznych danych transakcyjnych, będących częścią większego śledztwa dziennikarskiego. Wyniki wskazują obiecujący kierunek dla rozwoju nowoczesnych hybrydowych narzędzi do analizy podejrzanych transakcji, opartych na heterogenicznych sieciach grafowych.

Źródło:

Econometrics. Ekonometria. Advances in Applied Data Analytics; 2024, 28, 2; 32-49
1507-3866

Pojawia się w:

Econometrics. Ekonometria. Advances in Applied Data Analytics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 59.

Tytuł:

Industrial Application of Deep Neural Network for Aluminum Casting Defect Detection in Case of Unbalanced Dataset

Autorzy:

Awtoniuk, Michał
Majerek, Dariusz
Myziak, Artur
Gajda, Cyprian

Powiązania:

https://bibliotekanauki.pl/articles/2204946.pdf

Data publikacji:

2022

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

machine learning
deep neural network
classification
casting defect
casting defect detection

Opis:

We have developed a deep neural network for casting defect detection. The approach is original because it assumes the use of data related to the casting manufacturing process, i.e. measurement signals from the casting machine, rather than data describing the finished casting, e.g. images. The defects are related to the production of car engine heads made of silumin. In the current research we focused on the detection of defects related to the leakage of the casting. The data came from production plant in Poland. The dataset was unbalanced. It included nearly 38,500 observations, of which only 4% described a leak event. The work resulted in a deep network consisting of 22 layers. We assessed the classification accuracy using a ROC curve, an AUC index and a confusion matrix. The AUC value was 0.97 and 0.949 for the learning and testing dataset, respectively. The model allowed for an ex-post analysis of the casting process. The analysis was based on Shapley values. This makes it possible not only to detect the occurrence of a defect but also to give potential reasons for the appearance of a casting leak.

Źródło:

Advances in Science and Technology. Research Journal; 2022, 16, 5; 120--128
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 60.

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: 61.

Tytuł:

Artificial intelligence approach for detecting material deterioration in hybrid building constructions

Autorzy:

Chesnokov, Andrei V.
Mikhailov, Vitalii V.
Dolmatov, Ivan V.

Powiązania:

https://bibliotekanauki.pl/articles/29520106.pdf

Data publikacji:

2021

Wydawca:

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

Tematy:

hybrid construction
material deterioration
artificial neural network
semi-supervised machine learning

Opis:

Hybrid constructions include heterogeneous materials with different behaviors under load. The aim is to achieve a so-called synergistic effect when the advantages of particular structural elements complement each other in a unified system. The building constructions considered in the research include high-strength steel cables, fiberglass rods, and flexible polymer membranes. The membrane is attached to the rods which have been elastically bent from the initially straight shape into an arch-like form. Structural materials inevitably deteriorate during a long operational period. The present study focuses on detecting material deterioration using Artificial Neural Networks (ANNs), which belong to the scope of intelligent techniques for data analysis. Appropriate ANN structures and required features are proposed. A semi-supervised learning strategy is used. The approach allows the training of the networks with normal data only derived from the construction without defects. Material degradationis detected by the level of reconstruction error produced by the network given the input data. The work contributes to the field of structural health monitoring of hybrid building constructions. It provides the opportunity to detect material deterioration given the forces in particular structural elements.

Źródło:

Computer Methods in Materials Science; 2021, 21, 2; 83-94
2720-4081
2720-3948

Pojawia się w:

Computer Methods in Materials Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 62.

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: 63.

Tytuł:

A Study of Correlation between Fishing Activity and AIS Data by Deep Learning

Autorzy:

Shen, K. Y.
Chu, Y. J.
Chang, S. J.
Chang, S. M.

Powiązania:

https://bibliotekanauki.pl/articles/1841621.pdf

Data publikacji:

2020

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

AIS Data
deep learning framework
learning methods
Recurrent Neural Network
(RNN)
Automatic Identification System
(AIS)
fishing operation

Opis:

Previous researches on the prediction of fishing activities mainly rely on the speed over ground (SOG) as the referential attribute to determine whether the vessel is navigating or in fishing operation. Since more and more fishing vessels install Automatic Identification System (AIS) either voluntarily or under regulatory requirement, data collected from AIS in real time provide more attributes than SOG which may be utilized to improve the prediction. To be specific, the ships' trajectory patterns and the changes in course become available and should be considered. This paper aims to improve the accuracy in the identification of fishing activities. First, we do feature extraction from the AIS data of coastal waters around Taiwan and build a Recurrent Neural Network (RNN) model. Then, the activity data of fishing vessels are divided into fishing and non-fishing. Finally, based on the testing by feeding various fishing activity data, we can identify the fishing status automatically.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 3; 527-531
2083-6473
2083-6481

Pojawia się w:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 64.

Tytuł:

Multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on visual cues

Autorzy:

Jadhav, Nagesh
Sugandhi, Rekha

Powiązania:

https://bibliotekanauki.pl/articles/2086876.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

deep learning
convolution neural network
emotion recognition
transfer learning
late fusion
uczenie głębokie
konwolucyjna sieć neuronowa
rozpoznawanie emocji

Opis:

In the domain of affective computing different emotional expressions play an important role. To convey the emotional state of human emotions, facial expressions or visual cues are used as an important and primary cue. The facial expressions convey humans affective state more convincingly than any other cues. With the advancement in the deep learning techniques, the convolutional neural network (CNN) can be used to automatically extract the features from the visual cues; however variable sized and biased datasets are a vital challenge to be dealt with as far as implementation of deep models is concerned. Also, the dataset used for training the model plays a significant role in the retrieved results. In this paper, we have proposed a multi-model hybrid ensemble weighted adaptive approach with decision level fusion for personalized affect recognition based on the visual cues. We have used a CNN and pre-trained ResNet-50 model for the transfer learning. VGGFace model’s weights are used to initialize weights of ResNet50 for fine-tuning the model. The proposed system shows significant improvement in test accuracy in affective state recognition compared to the singleton CNN model developed from scratch or transfer learned model. The proposed methodology is validated on The Karolinska Directed Emotional Faces (KDEF) dataset with 77.85% accuracy. The obtained results are promising compared to the existing state of the art methods.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 6; e138819, 1--11
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: 65.

Tytuł:

Zastosowanie uczenia maszynowego w budowie interfejsu sterowanego głosem na przykładzie odtwarzacza muzyki
Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

Autorzy:

Basiakowski, Jakub

Powiązania:

https://bibliotekanauki.pl/articles/98114.pdf

Data publikacji:

2019

Wydawca:

Politechnika Lubelska. Instytut Informatyki

Tematy:

uczenie maszynowe
sieć neuronowa
rozpoznawanie głosu
machine learning
neural network
speech recognition

Opis:

Poniższy artykuł przedstawia wyniki badań wpływu zastosowania uczenia maszynowego w budowie interfejsu sterowanego głosem. Do analizy wykorzystane zostały dwa różne modele: jednokierunkowa sieć neuronowa zawierająca jedną warstwę ukrytą oraz bardziej skomplikowana konwolucyjna sieć neuronowa. Dodatkowo wykonane zostało porównanie modeli użytych w celu realizacji badań pod względem jakości oraz przebiegu treningu.
The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.

Źródło:

Journal of Computer Sciences Institute; 2019, 13; 302-309
2544-0764

Pojawia się w:

Journal of Computer Sciences Institute

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 66.

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: 67.

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

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Skocz do pozycji: 68.

Tytuł:

Falcon optimization algorithm for bayesian network structure learning

Autorzy:

Kareem, Shahab Wahhab
Okur, Mehmet Cudi

Powiązania:

https://bibliotekanauki.pl/articles/2097968.pdf

Data publikacji:

2021

Wydawca:

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

Tematy:

Bayesian network
global search
falcon optimization algorithm
structure learning
search and score

Opis:

In machine-learning, some of the helpful scientific models during the production of a structure of knowledge are Bayesian networks. They can draw the relationships of probabilistic dependency among many variables. The score and search method is a tool that is used as a strategy for learning the structure of a Bayesian network. The authors apply the falcon optimization algorithm (FOA) to the learning structure of a Bayesian network. This paper has employed reversing, deleting, moving, and inserting to obtain the FOA for approaching the optimal solution of a structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is associated with pigeon-inspired optimization, greedy search, and simulated annealing that apply the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques by utilizing several benchmark data sets. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values).

Źródło:

Computer Science; 2021, 22 (4); 553--569
1508-2806
2300-7036

Pojawia się w:

Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 69.

Tytuł:

Hospitalization patient forecasting based on multi-task deep learning

Autorzy:

Zhou, Min
Huang, Xiaoxiao
Liu, Haipeng
Zheng, Dingchang

Powiązania:

https://bibliotekanauki.pl/articles/2201025.pdf

Data publikacji:

2023

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

hospitalization patient
neural network
multitask learning
pacjent hospitalizowany
sieć neuronowa
nauka wielozadaniowa

Opis:

Forecasting the number of hospitalization patients is important for hospital management. The number of hospitalization patients depends on three types of patients, namely, admission patients, discharged patients, and inpatients. However, previous works focused on one type of patients rather than the three types of patients together. In this paper, we propose a multi-task forecasting model to forecast the three types of patients simultaneously. We integrate three neural network modules into a unified model for forecasting. Besides, we extract date features of admission and discharged patient flows to improve forecasting accuracy. The algorithm is trained and evaluated on a real-world data set of a one-year daily observation of patient numbers in a hospital. We compare the performance of our model with eight baselines over two real-word data sets. The experimental results show that our approach outperforms other baseline algorithms significantly.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2023, 33, 1; 151--162
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: 70.

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: 71.

Tytuł:

Application of iterative learning control for ripple torque compensation in PMSM drive

Autorzy:

Wójcik, Adrian
Pajchrowski, Tomasz

Powiązania:

https://bibliotekanauki.pl/articles/140797.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

ripple torque
iterative learning control
artificial neural network
permanent magnet synchronous motor

Opis:

The aim of the studywas to find an effective method of ripple torque compensation for a direct drive with a permanent magnet synchronous motor (PMSM) without time- consuming drive identification. The main objective of the research on the development of a methodology for the proper teaching a neural network was achieved by the use of iterative learning control (ILC), correct estimation of torque and spline interpolation. The paper presents the structure of the drive system and the method of its tuning in order to reduce the torque ripple, which has a significant effect on the uneven speed of the servo drive. The proposed structure of the PMSM in the dq axis is equipped with a neural compensator. The introduced iterative learning control was based on the estimation of the ripple torque and spline interpolation. The structurewas analyzed and verified by simulation and experimental tests. The elaborated structure of the drive system and method of its tuning can be easily used by applying a microprocessor system available now on the market. The proposed control solution can be made without time-consuming drive identification, which can have a great practical advantage. The article presents a new approach to proper neural network training in cooperation with iterative learning for repetitive motion systems without time-consuming identification of the motor.

Źródło:

Archives of Electrical Engineering; 2019, 68, 2; 309-324
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 72.

Tytuł:

Machine learning methods for diagnosing the causes of die-casting defects

Autorzy:

Okuniewska, Alicja
Perzyk, Marcin
Kozłowski, Jacek

Powiązania:

https://bibliotekanauki.pl/articles/29519775.pdf

Data publikacji:

2023

Wydawca:

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

Tematy:

fault diagnosis
machine learning tools
neural network
classification trees
support vector machine

Opis:

The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world’s leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.

Źródło:

Computer Methods in Materials Science; 2023, 23, 2; 45-56
2720-4081
2720-3948

Pojawia się w:

Computer Methods in Materials Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 73.

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: 74.

Tytuł:

Deep Neural Network for Supervised Single-Channel Speech Enhancement

Autorzy:

Saleem, Nasir
Irfan Khattak, Muhammad
Ali, Muhammad Yousaf
Shafi, Muhammad

Powiązania:

https://bibliotekanauki.pl/articles/177497.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

deep neural network
intelligibility
speech enhancement
speech quality
supervised learning
Wiener filtering

Opis:

Speech enhancement is fundamental for various real time speech applications and it is a challenging task in the case of a single channel because practically only one data channel is available. We have proposed a supervised single channel speech enhancement algorithm in this paper based on a deep neural network (DNN) and less aggressive Wiener filtering as additional DNN layer. During the training stage the network learns and predicts the magnitude spectrums of the clean and noise signals from input noisy speech acoustic features. Relative spectral transform-perceptual linear prediction (RASTA-PLP) is used in the proposed method to extract the acoustic features at the frame level. Autoregressive moving average (ARMA) filter is applied to smooth the temporal curves of extracted features. The trained network predicts the coefficients to construct a ratio mask based on mean square error (MSE) objective cost function. The less aggressive Wiener filter is placed as an additional layer on the top of a DNN to produce an enhanced magnitude spectrum. Finally, the noisy speech phase is used to reconstruct the enhanced speech. The experimental results demonstrate that the proposed DNN framework with less aggressive Wiener filtering outperforms the competing speech enhancement methods in terms of the speech quality and intelligibility.

Źródło:

Archives of Acoustics; 2019, 44, 1; 3-12
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 75.

Tytuł:

A cloud-based urban monitoring system by using a quadcopter and intelligent learning techniques

Autorzy:

Khanmohammadi, Sohrab
Samadi, Mohammad

Powiązania:

https://bibliotekanauki.pl/articles/27314186.pdf

Data publikacji:

2022

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

urban monitoring
cloud computing
quadcopter
deep learning
fuzzy system
image processing
pattern recognition
bayesian network
intelligent techniques
learning systems

Opis:

The application of quadcopter and intelligent learning techniques in urban monitoring systems can improve flexibility and efficiency features. This paper proposes a cloud-based urban monitoring system that uses deep learning, fuzzy system, image processing, pattern recognition, and Bayesian network. The main objectives of this system are to monitor climate status, temperature, humidity, and smoke, as well as to detect fire occurrences based on the above intelligent techniques. The quadcopter transmits sensing data of the temperature, humidity, and smoke sensors, geographical coordinates, image frames, and videos to a control station via RF communications. In the control station side, the monitoring capabilities are designed by graphical tools to show urban areas with RGB colors according to the predetermined data ranges. The evaluation process illustrates simulation results of the deep neural network applied to climate status and effects of the sensors’ data changes on climate status. An illustrative example is used to draw the simulated area using RGB colors. Furthermore, circuit of the quadcopter side is designed using electric devices.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 2; 11--19
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: 76.

Tytuł:

Some Aspects of Increasing the Effectiveness and Comfort of the Scientific and Educational Process in University Electronic Environment - A Research Report

Autorzy:

Smyrnova-Trybulska, Eugenia

Powiązania:

https://bibliotekanauki.pl/articles/2004921.pdf

Data publikacji:

2016-09-30

Wydawca:

Wydawnictwo Adam Marszałek

Tematy:

ICT
e-environment
international research network
survey
educational and research activities
e-learning

Opis:

The research presented in the article seems to confirm the assumption that e-learning and ICT development contribute to the quality of educational services, to the development of information society competences and to the increased competitiveness of institutions of science and education. E-learning participants aim at: increasing comfort in the scientific and educational process; lifelong learning goals; the personalization of education; the formation of new scientific and educational cooperation and intercultural competence; self-fulfilment in education and work; increased openness of the scientific and educational environment; and enhancing self-organizational effects which support the sustainable development of the university environment. The research was conducted at the University of Silesia within the framework of IRNet project.

Źródło:

The New Educational Review; 2016, 45; 259-270
1732-6729

Pojawia się w:

The New Educational Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 77.

Tytuł:

Synchrony state generation : an approach using stochastic synapses

Autorzy:

El-Laithy, K.
Bogdan, M.

Powiązania:

https://bibliotekanauki.pl/articles/91844.pdf

Data publikacji:

2011

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

temporal synchrony
artificial neural network
stochastic synapses
synchrony state generation
Hebbian-based learning

Opis:

In this study, the generation of temporal synchrony within an artificial neural network is examined considering a stochastic synaptic model. A network is introduced and driven by Poisson distributed trains of spikes along with white-Gaussian noise that is added to the internal synaptic activity representing the background activity (neuronal noise). A Hebbian-based learning rule for the update of synaptic parameters is introduced. Only arbitrarily selected synapses are allowed to learn, i.e. update parameter values. Results show that a network using such a framework is able to achieve different states of synchrony via learning. Thus, the plausibility of using stochastic-based models in modeling the neural process is supported. It is also consistent with arguments claiming that synchrony is a part of the memory-recall process and copes with the accepted framework in biological neural systems.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 17-25
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: 78.

Tytuł:

Recurrent neural identification and control of a continuous bioprocess via first and second order learning

Autorzy:

Baruch, I.
Mariaca-Gaspar, C. R.

Powiązania:

https://bibliotekanauki.pl/articles/385133.pdf

Data publikacji:

2010

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

backpropagation learning
direct adaptive neural control
indirect adaptive sliding mode control
Kalman filter recurrent neural network identifier
Levenberg-Marquardt learning

Opis:

This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
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: 79.

Tytuł:

Reinforcement Learning in Ship Handling

Autorzy:

Łącki, M.

Powiązania:

https://bibliotekanauki.pl/articles/117361.pdf

Data publikacji:

2008

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

Ship Handling
Reinforcement Learning
Machine Learning Techniques
Manoeuvring
Restricted Waters
Markov Decision Process (MDP)
Artificial Neural Network (ANN)
multi-agent environment

Opis:

This paper presents the idea of using machine learning techniques to simulate and demonstrate learning behaviour in ship manoeuvring. Simulated model of ship is treated as an agent, which through environmental sensing learns itself to navigate through restricted waters selecting an optimum trajectory. Learning phase of the task is to observe current state and choose one of the available actions. The agent gets positive reward for reaching destination and negative reward for hitting an obstacle. Few reinforcement learning algorithms are considered. Experimental results based on simulation program are presented for different layouts of possible routes within restricted area.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2008, 2, 2; 157-160
2083-6473
2083-6481

Pojawia się w:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

Dostawca treści:

Biblioteka Nauki

/5

Artykuł

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Skocz do pozycji: 80.

Tytuł:

A comparative study on performance of basic and ensemble classifiers with various datasets

Autorzy:

Gunakala, Archana
Shahid, Afzal Hussain

Powiązania:

https://bibliotekanauki.pl/articles/30148255.pdf

Data publikacji:

2023

Wydawca:

Polskie Towarzystwo Promocji Wiedzy

Tematy:

classification
Naïve Bayes
neural network
Support Vector Machine
Decision Tree
ensemble learning
Random Forest

Opis:

Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen based on the model's performance and execution time. This paper compares and analyzes the performance of basic as well as ensemble classifiers utilizing 10-fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from Kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01%. The proposed ensemble combinations outperformed the conven¬tional models for few datasets.

Źródło:

Applied Computer Science; 2023, 19, 1; 107-132
1895-3735
2353-6977

Pojawia się w:

Applied Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 81.

Tytuł:

Remaining useful life prediction with insufficient degradation data based on deep learning approach

Autorzy:

Lyu, Yi
Jiang, Yijie
Zhang, Qichen
Chen, Ci

Powiązania:

https://bibliotekanauki.pl/articles/2038109.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

deep learning
remaining useful life
degradation data
data amplification
cycle-consistent generative adversarial network

Opis:

Remaining useful life (RUL) prediction plays a crucial role in decision-making in conditionbased maintenance for preventing catastrophic field failure. For degradation-failed products, the data of performance deterioration process are the key for lifetime estimation. Deep learning has been proved to have excellent performance in RUL prediction given that the degradation data are sufficiently large. However, in some applications, the degradation data are insufficient, under which how to improve the prediction accuracy is yet a challenging problem. To tackle such a challenge, we propose a novel deep learning-based RUL prediction framework by amplifying the degradation dataset. Specifically, we leverage the cycle-consistent generative adversarial network to generate the synthetic data, based on which the original degradation dataset is amplified so that the data characteristics hidden in the sample space could be captured. Moreover, the sliding time window strategy and deep bidirectional long short-term memory network are employed to complete the RUL prediction framework. We show the effectiveness of the proposed method by running it on the turbine engine data set from the National Aeronautics and Space Administration. The comparative experiments show that our method outperforms a case without the use of the synthetically generated data.

Źródło:

Eksploatacja i Niezawodność; 2021, 23, 4; 745-756
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 82.

Tytuł:

Multicriteria Oppositional-Learnt Dragonfly Resource-Optimized QoS Driven Channel Selection for CRNs

Autorzy:

Sirisha Devi, Ch. S. N.
Maloj, Suman

Powiązania:

https://bibliotekanauki.pl/articles/2174446.pdf

Data publikacji:

2022

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

cognitive ratio network (CRN)
multicriteria dragonfly optimization
oppositional learning
optimal available channel
QoS metric

Opis:

Cognitive radio networks (CRNs) allow their users to achieve adequate QoS while communicating. The major concern related to CRN is linked to guaranteeing free channel selection to secondary users (SUs) in order to maintain the network’s throughput. Many techniques have been designed in the literature for channel selection in CRNs, but the throughput of the network has not been enhanced yet. Here, an efficient technique, known as multicriteria oppositional-learnt dragonfly resourceoptimized QoS-driven channel selection (MOLDRO-QoSDCS) is proposed to select the best available channel with the expected QoS metrics. The MOLDRO-QoSDCS technique is designed to improve energy efficiency and throughput, simultaneously reducing the sensing time. By relying on oppositional-learnt multiobjective dragonfly optimization, the optimal available channel is selected depending on signal-to-noise ratio, power consumption, and spectrum utilization. In the optimization process, the population of the available channels is initialized. Then, using multiple criteria, the fitness function is determined and the available channel with the best resource availability is selected. Using the selected optimal channel, data transmission is effectively performed to increase the network’s throughput and to minimize the sensing time. The simulated outputs obtained with the use of Matlab are compared with conventional algorithms in order to verify the performance of the solution. The MOLDRO-QoSDCS technique performs better than other methods in terms of throughput, sensing time, and energy efficiency.

Źródło:

Journal of Telecommunications and Information Technology; 2022, 4; 41--46
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: 83.

Tytuł:

Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models

Autorzy:

Xu, Jun
Wei, Yumeng
Wang, Aichun
Zhao, Heng
Lefloch, Damien

Powiązania:

https://bibliotekanauki.pl/articles/2200761.pdf

Data publikacji:

2022

Wydawca:

Sieć Badawcza Łukasiewicz - Instytut Biopolimerów i Włókien Chemicznych

Tematy:

e-commerce
clothing image classification
traditional machine learning
CNN
HOG
SVM
small VGG network

Opis:

Clothing image in the e-commerce industry plays an important role in providing customers with information. This paper divides clothing images into two groups: pure clothing images and dressed clothing images. Targeting small and medium-sized clothing companies or merchants, it compares traditional machine learning and deep learning models to determine suitable models for each group. For pure clothing images, the HOG+SVM algorithm with the Gaussian kernel function obtains the highest classification accuracy of 91.32% as compared to the Small VGG network. For dressed clothing images, the CNN model obtains a higher accuracy than the HOG+SVM algorithm, with the highest accuracy rate of 69.78% for the Small VGG network. Therefore, for end-users with only ordinary computing processors, it is recommended to apply the traditional machine learning algorithm HOG+SVM to classify pure clothing images. The classification of dressed clothing images is performed using a more efficient and less computationally intensive lightweight model, such as the Small VGG network.

Źródło:

Fibres & Textiles in Eastern Europe; 2022, 5 (151); 66--78
1230-3666
2300-7354

Pojawia się w:

Fibres & Textiles in Eastern Europe

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 84.

Tytuł:

Selected technical issues of deep neural networks for image classification purposes

Autorzy:

Grochowski, Michał
Kwasigroch, A.
Mikołajczyk, A.

Powiązania:

https://bibliotekanauki.pl/articles/200871.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

deep neural network
deep learning
image classification
batch normalization
transfer learning
dropout
sieć neuronowa
klasyfikacja obrazów
normalizacja
transfer nauki
uczenie głębokie

Opis:

In recent years, deep learning and especially deep neural networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the convolutional neural networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good generalization abilities. Therefore, a number of methods have been proposed by the researchers to deal with these problems. In this paper, we present the results of applying different, recently developed methods to improve deep neural network training and operating. We decided to focus on the most popular CNN structures, namely on VGG based neural networks: VGG16, VGG11 and proposed by us VGG8. The tests were conducted on a real and very important problem of skin cancer detection. A publicly available dataset of skin lesions was used as a benchmark. We analyzed the influence of applying: dropout, batch normalization, model ensembling, and transfer learning. Moreover, the influence of the type of activation function was checked. In order to increase the objectivity of the results, each of the tested models was trained 6 times and their results were averaged. In addition, in order to mitigate the impact of the selection of learning, test and validation sets, k-fold validation was applied.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2019, 67, 2; 363-376
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: 85.

Tytuł:

Ethnography of Virtual Phenomena and Processes on the Internet

Autorzy:

Juszczyk, Stanisław

Powiązania:

https://bibliotekanauki.pl/articles/2031693.pdf

Data publikacji:

2014-06-30

Wydawca:

Wydawnictwo Adam Marszałek

Tematy:

virtual ethnography
global network
communication process
social networks
distance learning
virtual support groups
cyberbullying

Opis:

The paper presents the ethnography of virtual phenomena and processes, conducted in the social, educational and cultural spheres of the Internet. It describes the most important features of the global network and shows all its aspects which are the subject of ethnographic studies at the Faculty of Pedagogy and Psychology of the University of Silesia in Katowice, i.e. the communication process and its semiotic character, the process of creating social networks, education supported by social media, virtual self-help groups for stigmatised people, and cyberbullying.

Źródło:

The New Educational Review; 2014, 36; 206-216
1732-6729

Pojawia się w:

The New Educational Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 86.

Tytuł:

Roles of Music-Making in the Process of Cross-Cultural Adaptation: A Case of International Students in Wrocław

Autorzy:

Alptekin, Emre Can

Powiązania:

https://bibliotekanauki.pl/articles/2196932.pdf

Data publikacji:

2022-10-06

Wydawca:

Komisja Nauk Filologicznych Polskiej Akademii Nauk, Oddział we Wrocławiu

Tematy:

music-making
sociocultural adaptation
psychological adjustment
culture learning theory
social network
cross-cultural transition

Opis:

With the intensifying flow of academically motivated people between countries, the significance of research on cross-cultural adaptation increases. Although the problems and difficulties caused by cultural differences have been researched extensively, this research focused on a common practice among different cultures: participative music making in an intercultural context. Therefore, the current study explores how participative music-making shapes international students’ cross-cultural experiences in Wroclaw. For this purpose, the relevance between international students’ cross-cultural adaptation and music-making as a social activity in Poland is examined. The required data were gathered through in-depth interviews with six students from various countries who made music as a collective activity during their transnational accommodation. The collected data is analysed by the inductive coding approach to explore the commonalities in the international students’ experiences. Findings concluded that collective music making shapes music-maker students’ cross-cultural experiences by not merely helping them gain a specific social network but also contributing to their financial income and mood states, and finally, privileged behaviour by the host country members towards these students.

Źródło:

Academic Journal of Modern Philology; 2022, 15; 21-32
2299-7164
2353-3218

Pojawia się w:

Academic Journal of Modern Philology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 87.

Tytuł:

An autoencoder-enhanced stacking neural network model for increasing the performance of intrusion detection

Autorzy:

Brunner, Csaba
Kő, Andrea
Fodor, Szabina

Powiązania:

https://bibliotekanauki.pl/articles/2147134.pdf

Data publikacji:

2022

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

intrusion detection
neural network
ensemble classifiers
hyperparameter optimization
sparse autoencoder
NSL-KDD
machine learning

Opis:

Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2022, 12, 2; 149--163
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: 88.

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: 89.

Tytuł:

Designing Smart Antennas Using Machine Learning Algorithms

Autorzy:

Samantaray, Barsa
Das, Kunal Kumar
Roy, Jibendu Sekhar

Powiązania:

https://bibliotekanauki.pl/articles/27312957.pdf

Data publikacji:

2023

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

artificial neural network
decision tree
ensemble algorithm
machine learning
smart antenna
support vector machine

Opis:

Smart antenna technologies improve spectral efficiency, security, energy efficiency, and overall service quality in cellular networks by utilizing signal processing algorithms that provide radiation beams to users while producing nulls for interferers. In this paper, the performance of such ML solutions as the support vector machine (SVM) algorithm, the artificial neural network (ANN), the ensemble algorithm (EA), and the decision tree (DT) algorithm used for forming the beam of smart antennas are compared. A smart antenna array made up of 10 half-wave dipoles is considered. The ANN method is better than the remaining approaches when it comes to achieving beam and null directions, whereas EA offers better performance in terms of reducing the side lobe level (SLL). The maximum SLL is achieved using EA for all the user directions. The performance of the ANN algorithm in terms of forming the beam of a smart antenna is also compared with that of the variable-step size adaptive algorithm.

Źródło:

Journal of Telecommunications and Information Technology; 2023, 4; 46--52
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: 90.

Tytuł:

Opracowanie koncepcji i implementacja modelu rozpoznawania obrazu z wykorzystaniem elementów sztucznej inteligencji
Development of the concept and implementation of an image recognition model using elements of artificial intelligence

Autorzy:

Sierżantowicz, Anna
Ptasznik, Andrzej

Powiązania:

https://bibliotekanauki.pl/articles/1397486.pdf

Data publikacji:

2020

Wydawca:

Warszawska Wyższa Szkoła Informatyki

Tematy:

rozpoznawanie obrazu
sztuczne sieci neuronowe
sieci splotowe
InceptionV3
transfer wiedzy
computer vision
artificial neural network
connvolutional neural network
transfer learning

Opis:

W niniejszym artykule przedstawiono koncepcję i implementację modelu do rozpoznawania ras psów na podstawie zdjęcia. Do realizacji zadania wykorzystano model głębokiej sieci neuronowej bazujący na strukturze InceptionV3. Sieć została wytrenowana i przetestowana na zbiorze przypadków uczących liczącym ponad 20 tys. zdjęć 120 ras psów z zastosowaniem transferu wiedzy. Zbadano również wpływ jakości zdjęć na wyniki klasyfikacji. Sieć uzyskała bardzo dobre rezultaty zarówno w przypadku analizy typowych, jak i nietypowych zdjęć.
This article presents the concept and implementation of a model for recognizing dog breeds based on an input image. The task was performed with the use of a deep neural network model based on the InceptionV3 structure. The neural network has been trained and tested on a dataset counting more than 20,000 images of 120 dog breeds using transfer learning technique. The impact of image quality on classification results was also examined. The model obtained very good results in the analysis of both typical and unusual input images.

Źródło:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2020, 14, 23; 7-26
1896-396X
2082-8349

Pojawia się w:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 91.

Tytuł:

Development of a neural statistical model for the prediction of relative humidity levels in the region of Rabat-Kenitra, North West Morocco

Autorzy:

El Azhari, Kaoutar
Abdallaoui, Badreddine
Dehbi, Ali
Abdalloui, Abdelaziz
Zineddine, Hamid

Powiązania:

https://bibliotekanauki.pl/articles/2174362.pdf

Data publikacji:

2022

Wydawca:

Instytut Technologiczno-Przyrodniczy

Tematy:

artificial neural network
ANN
learning algorithm
multi-layer perceptron
MLP
modelling
Rabat-Kenitra
relative humidity

Opis:

This article accounts for the development of a powerful artificial neural network (ANN) model, designed for the prediction of relative humidity levels, using other meteorological parameters such as the maximum temperature, minimum temperature, precipitation, wind speed, and intensity of solar radiation in the Rabat-Kenitra region (a coastal area where relative humidity is a real concern). The model was applied to a database containing a daily history of five meteorological parameters collected by nine stations covering this region from 1979 to mid-2014. It has been demonstrated that the best performing three-layer (input, hidden, and output) ANN mathematical model for the prediction of relative humidity in this region is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, with an architecture of [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer, was able to estimate relative humidity values that were very close to those observed. This was affirmed by a low mean squared error (MSE) and a high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

Źródło:

Journal of Water and Land Development; 2022, 54; 13--20
1429-7426
2083-4535

Pojawia się w:

Journal of Water and Land Development

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 92.

Tytuł:

Keystroke dynamics analysis using machine learning methods

Autorzy:

Shabliy, Nataliya
Lupenko, Serhii
Lutsyk, Nadiia
Yasniy, Oleh
Malyshevska, Olha

Powiązania:

https://bibliotekanauki.pl/articles/1956034.pdf

Data publikacji:

2021

Wydawca:

Polskie Towarzystwo Promocji Wiedzy

Tematy:

keystroke dynamics analysis
Machine Learning
Neural Network
Supervised Learning
classification problem
analiza dynamiki uderzeń klawiszy
uczenie maszynowe
sieć neuronowa
uczenie nadzorowane
problem klasyfikacji

Opis:

The primary objective of the paper was to determine the user based on its keystroke dynamics using the methods of machine learning. Such kind of a problem can be formulated as a classification task. To solve this task, four methods of supervised machine learning were employed, namely, logistic regression, support vector machines, random forest, and neural network. Each of three users typed the same word that had 7 symbols 600 times. The row of the dataset consists of 7 values that are the time period during which the particular key was pressed. The ground truth values are the user id. Before the application of machine learning classification methods, the features were transformed to z-score. The classification metrics were obtained for each applied method. The following parameters were determined: precision, recall, f1-score, support, prediction, and area under the receiver operating characteristic curve (AUC). The obtained AUC score was quite high. The lowest AUC score equal to 0.928 was achieved in the case of linear regression classifier. The highest AUC score was in the case of neural network classifier. The method of support vector machines and random forest showed slightly lower results as compared with neural network method. The same pattern is true for precision, recall and F1-score. Nevertheless, the obtained classification metrics are quite high in every case. Therefore, the methods of machine learning can be efficiently used to classify the user based on keystroke patterns. The most recommended method to solve such kind of a problem is neural network.

Źródło:

Applied Computer Science; 2021, 17, 4; 75-83
1895-3735

Pojawia się w:

Applied Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 93.

Tytuł:

Machine learning methods applied to sea level predictions in the upper part of a tidal estuary

Autorzy:

Guillou, N.
Chapalain, G.

Powiązania:

https://bibliotekanauki.pl/articles/2078822.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Instytut Oceanologii PAN

Tematy:

multiple regression model
artificial neural network
multilayer perceptron
regression function
machine learning algorithm
sea level

Opis:

Sea levels variations in the upper part of estuary are traditionally approached by relying on refined numerical simulations with high computational cost. As an alternative efficient and rapid solution, we assessed here the performances of two types of machine learning algorithms: (i) multiple regression methods based on linear and polynomial regression functions, and (ii) an artificial neural network, the multilayer perceptron. These algorithms were applied to three-year observations of sea levels maxima during high tides in the city of Landerneau, in the upper part of the Elorn estuary (western Brittany, France). Four input variables were considered in relation to tidal and coastal surge effects on sea level: the French tidal coefficient, the atmospheric pressure, the wind velocity and the river discharge. Whereas a part of these input variables derived from large-scale models with coarse spatial resolutions, the different algorithms showed good performances in this local environment, thus being able to capture sea level temporal variations at semi-diurnal and spring-neap time scales. Predictions improved furthermore the assessment of inundation events based so far on the exploitation of observations or numerical simulations in the downstream part of the estuary. Results obtained exhibited finally the weak influences of wind and river discharges on inundation events.

Źródło:

Oceanologia; 2021, 63, 4; 531-544
0078-3234

Pojawia się w:

Oceanologia

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 94.

Tytuł:

2D Cadastral Coordinate Transformation using extreme learning machine technique

Autorzy:

Ziggah, Y. Y.
Issaka, Y.
Laari, P. B.
Hui, Z.

Powiązania:

https://bibliotekanauki.pl/articles/145372.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

transformacja współrzędnych
sieci neuronowe
dane geodezyjne
sieć radialna
coordinate transformation
extreme learning machine
backpropagation neural network
radial basis function neural network
geodetic datum

Opis:

Land surveyors, photogrammetrists, remote sensing engineers and professionals in the Earth sciences are often faced with the task of transferring coordinates from one geodetic datum into another to serve their desired purpose. The essence is to create compatibility between data related to different geodetic reference frames for geospatial applications. Strictly speaking, conventional techniques of conformal, affine and projective transformation models are mostly used to accomplish such task. With developing countries like Ghana where there is no immediate plans to establish geocentric datum and still rely on the astro-geodetic datums as it national mapping reference surface, there is the urgent need to explore the suitability of other transformation methods. In this study, an effort has been made to explore the proficiency of the Extreme Learning Machine (ELM) as a novel alternative coordinate transformation method. The proposed ELM approach was applied to data found in the Ghana geodetic reference network. The ELM transformation result has been analysed and compared with benchmark methods of backpropagation neural network (BPNN), radial basis function neural network (RBFNN), two-dimensional (2D) affine and 2D conformal. The overall study results indicate that the ELM can produce comparable transformation results to the widely used BPNN and RBFNN, but better than the 2D affine and 2D conformal. The results produced by ELM has demonstrated it as a promising tool for coordinate transformation in Ghana.

Źródło:

Geodesy and Cartography; 2018, 67, 2; 321-343
2080-6736
2300-2581

Pojawia się w:

Geodesy and Cartography

Dostawca treści:

Biblioteka Nauki

/5

Artykuł

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Skocz do pozycji: 95.

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: 96.

Tytuł:

Sztuczne sieci neuronowe jako motoryczna pamięć asocjacyjna ręki robota humanoidalnego
Artificial neural networks as a motorical associative memory for humanoid robot hand

Autorzy:

Olszewski, P.
Kamiński, W. A.

Powiązania:

https://bibliotekanauki.pl/articles/408803.pdf

Data publikacji:

2017

Wydawca:

Politechnika Lubelska. Wydawnictwo Politechniki Lubelskiej

Tematy:

sztuczna sieć neuronowa
metoda uczenia
robot humanoidalny
manipulator
artificial neural network
learning system
humanoid robot
manipulators

Opis:

Opisane badania dotyczą użycia sztucznych sieci neuronowych przy realizacji asocjacyjnej pamięci motorycznej zarządzającej ręką robota humanoidalnego. Zaproponowano model kognitywnego sterowania odwołujący się do struktur i mechanizmów przetwarzania znanych z badań neurofizjologicznych. Do realizacji asocjacji posłużono się dwiema różnymi sieciami: maszyną płynową i jednokierunkową siecią asocjacyjną podobną do BAM.
This paper relates to the artificial neural networks usage for the purpose of associative memory implementation managing humanoid robot hand. Refering to the structures and mechanisms of processing known from neurophysiological studies model of cognitive control was proposed. In the association realization process two different networks were used: a liquid state machine and a one-way associative network similar to BAM.

Źródło:

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska; 2017, 7, 3; 72-77
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: 97.

Tytuł:

Some improvements in the reinforcement learning of a mobile robot
Uczenie ze wzmocnieniem robotów mobilnych - propozycje usprawnień

Autorzy:

Pluciński, M.

Powiązania:

https://bibliotekanauki.pl/articles/153411.pdf

Data publikacji:

2010

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

uczenie ze wzmocnieniem
sieci neuronowe RBF
roboty mobilne
reinforcement learning
probabilistic RBF neural network
mobile robot

Opis:

The paper presents application of the reinforcement learning to autonomous mobile robot moving learning in an unknown, stationary environment. The robot movement policy was represented by a probabilistic RBF neural network. As the learning process was very slow or even impossible for complicated environments, there are presented some improvements, which were found out to be very effective in most cases.
W artykule zaprezentowane jest zastosowanie uczenia ze wzmocnieniem w poszukiwaniu strategii ruchu autonomicznego robota mobilnego w nieznanym, stacjonarnym środowisku. Zadaniem robota jest dotarcie do zadanego i znanego mu punktu docelowego jak najkrótszą drogą i bez kolizji z przeszkodami. Stan robota określa jego położenie w stałym (związanym ze środowiskiem) układzie współrzędnych, natomiast akcja wyznaczana jest jako zadany kierunek ruchu. Strategia robota zdefiniowana jest pośrednio za pomocą funkcji wartości, którą reprezentuje sztuczna sieć neuronowa typu RBF. Sieci tego typu są łatwe w uczeniu, a dodatkowo ich parametry umożliwiają wygodną interpretację realizowanego odwzorowania. Ponieważ w ogólnym przypadku uczenie robota jest bardzo trudne, a w skomplikowanych środowiskach praktycznie niemożliwe, stąd w artykule zaprezentowanych jest kilka propozycji jego usprawnienia. Opisane są eksperymenty: z wykorzystaniem ujemnych wzmocnień generowanych przez przeszkody, z zastosowaniem heurystycznych sposobów podpowiadania robotowi właściwych zachowań w "trudnych" sytuacjach oraz z wykorzystaniem uczenia stopniowego. Badania wykazały, że najlepsze efekty uczenia dało połączenie dwóch ostatnich technik.

Źródło:

Pomiary Automatyka Kontrola; 2010, R. 56, nr 12, 12; 1470-1473
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 98.

Tytuł:

Edukacja zdalna w czasie pandemii COVID-19 w doświadczeniach uczniów i uczennic: ocena relacji szkolnych i jej uwarunkowania
Distance education during the COVID-19 outbreak in the experiences of Polish students: the assessment of school relationships and its determinants

Autorzy:

Jaskulska, Sylwia
Jankowiak, Barbara
Marciniak, Mateusz
Klichowski, Michał

Powiązania:

https://bibliotekanauki.pl/articles/2130715.pdf

Data publikacji:

2021

Wydawca:

Uniwersytet Wrocławski. Wydział Nauk Historycznych i Pedagogicznych. Instytut Pedagogiki. Zakład Historii Edukacji

Tematy:

edukacja zdalna
pandemia COVID-19
uczniowie
relacje społeczne
distance learning
coronavirus outbreak
students
social relationships network

Opis:

Wprowadzenie. Relacje interpersonalne wysokiej jakości są czynnikiem chroniącym w sytuacji kryzysowej. Badania dotyczące okresu pandemii COVID-19 wskazują na związek oceny własnych sieci relacji i innych aspektów codziennego funkcjonowania. Dotyczy to także dzieci i młodzieży i jest jedną z kluczowych kwestii w profi laktyce negatywnych konsekwencji doświadczeń z okresu pandemii dla rozwoju i zdrowia psychicznego. Cel. Celem badań prezentowanych w tekście było poznanie szkolnych doświadczeń uczniów i uczennic doznawanych podczas pandemii w obszarze relacji (z kolegami i koleżankami z klasy, z wychowawcą/wychowawczynią, nauczycielkami i nauczycielami). Problemy badawcze dotyczyły uczniowskiej oceny tych relacji i jej uwarunkowań. Materiały i metody. Uczestnikami badania byli uczniowie i uczennice (N=1955) w wieku od 9 do 20 lat, korzystający z dziennika elektronicznego VULCAN, uczący się w szkołach podstawowych i średnich. Zastosowano metodę sondażu diagnostycznego. Zebrane dane poddano analizie statystycznej z wykorzystaniem statystyk opisowych, analizy częstości, testów istotności różnic (test niezależności chi-kwadrat). Wyniki. Uczniowie i uczennice najlepiej oceniają swoje relacje z wychowawcami. Prawie 70% uważa, że były one dobre przed pandemią i takie pozostały. Ponad 25% dostrzega pogorszenie relacji z kolegami i koleżankami na skutek pandemii i edukacji zdalnej – szczególnie dziewczęta i osoby uczące się w szkołach wiejskich.
Introduction. High-quality interpersonal relationships are a protective factor in a crisis situation. Research on the coronavirus (SARS-CoV-2) pandemic shows that there is a relationship between the personal assessment of one’s own social relationship network and other aspects of everyday functioning. This also applies to children and adolescents, and it becomes one of the key issues in preventing the negative consequences of pandemic experiences for their development and mental health. Aim. The aim of the research was to explore the students’ school experiences during the COVID-19 outbreak in the area of their relationships with classmates and teachers. Research problems concerned the students’ assessment of these relations and its determinants. Materials and methods. The participants were students (N = 1955) aged from 9 to 20, studying in primary and secondary schools. The survey method was used. The collected data were statistically analysed using descriptive statistics, frequency analysis, and significance difference tests (chi-square test). Results. Almost 70% of students claim that they have good relations with teachers and that they have remained unchanged during the COVID-19 pandemic. Over 25% of students noticed a deterioration in relations with their colleagues (especially girls, and pupils studying in rural area schools).

Źródło:

Wychowanie w Rodzinie; 2021, XXIV, (1/2021); 133-146
2082-9019

Pojawia się w:

Wychowanie w Rodzinie

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 99.

Tytuł:

Comparative study of learning methods for artificial neural network
Badania porównawcze metod uczenia sieci neuronowej

Autorzy:

Tiliouine, H.

Powiązania:

https://bibliotekanauki.pl/articles/153863.pdf

Data publikacji:

2007

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

metody uczenia
sieć neuronowa
neuronowy regulator napięcia
learning methods
artificial neural network (ANN)
neural voltage controller

Opis:

The paper presents a comparative study of various learning methods for artificial neural network. The methods are: the backpropagation BP, the recursive least squares RLS, the Zangwill's method ZGW and the method based on evolutionary algorithm EA. The study consists of evaluating the learning effectiveness of these methods and selecting the most efficient one to be used in the designing of an adaptive neural voltage controller for a synchronous generator.
W artykule przedstawiono wyniki badań porównawczych metod uczenia sieci neuronowych takich jak: metoda propagacji wstecznej błędów, rekurencyjna metoda najmniejszych kwadratów, metoda Zangwill'a, metoda algorytmów ewolucyjnych. Celem tych badań jest dobieranie najefektywniejszej metody uczenia do projektowania adaptacyjnego neuronowego regulatora napięcia generatora synchronicznego.

Źródło:

Pomiary Automatyka Kontrola; 2007, R. 53, nr 4, 4; 117-121
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 100.

Tytuł:

Artificial intelligence-based hybrid forecasting models for manufacturing systems

Autorzy:

Rosienkiewicz, Maria

Powiązania:

https://bibliotekanauki.pl/articles/1841698.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

artificial neural network
support vector machine
extreme learning machine
hybrid forecasting
production planning
maintenance
quality control

Opis:

The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.

Źródło:

Eksploatacja i Niezawodność; 2021, 23, 2; 263-277
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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