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:

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: 25.

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: 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

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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

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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

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