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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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
Tytuł:
Tomato disease detection model based on densenet and transfer learning
Autorzy:
Bakr, Mahmoud
Abdel-Gaber, Sayed
Nasr, Mona
Hazman, Maryam
Powiązania:
https://bibliotekanauki.pl/articles/2097440.pdf
Data publikacji:
2022
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
leaf disease detection
convolutional neural network
deep learning
transfer learning
Opis:
Plant diseases are a foremost risk to the safety of food. They have the potential to significantly reduce agricultural products quality and quantity. In agriculture sectors, it is the most prominent challenge to recognize plant diseases. In computer vision, the Convolutional Neural Network (CNN) produces good results when solving image classification tasks. For plant disease diagnosis, many deep learning architectures have been applied. This paper introduces a transfer learning based model for detecting tomato leaf diseases. This study proposes a model of DenseNet201 as a transfer learning-based model and CNN classifier. A comparison study between four deep learning models (VGG16, Inception V3, ResNet152V2 and DenseNet201) done in order to determine the best accuracy in using transfer learning in plant disease detection. The used images dataset contains 22930 photos of tomato leaves in 10 different classes, 9 disorders and one healthy class. In our experimental, the results shows that the proposed model achieves the highest training accuracy of 99.84% and validation accuracy of 99.30%.
Źródło:
Applied Computer Science; 2022, 18, 2; 56--70
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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
Artykuł
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