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Tytuł:
On different ways to classify Internet traffic : a short review of selected publications
O wielu sposobach klasyfikacji ruchu internetowego: krótki przegląd wybranych publikacji
Autorzy:
Foremski, P.
Powiązania:
https://bibliotekanauki.pl/articles/375768.pdf
Data publikacji:
2013
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
internet
traffic classification
machine learning
Opis:
Traffic classification is an important tool for network management. It reveals the source of observed network traffic and has many potential applications e.g. in Quality of Service, network security and traffic visualization. In the last decade, traffic classification evolved quickly due to the raise of peer-to-peer traffic. Nowadays, researchers still find new methods in order to withstand the rapid changes of the Internet. In this paper, we review 13 publications on traffic classification and related topics that were published during 2009-2012. We show diversify in recent algorithms and we highlight possible directions for the future research on traffic classification: relevance of multi-level classification, importance of experimental validation, and the need for common traffic datasets.
Artykuł prezentuje przegląd 13 wybranych prac z dziedziny klasyfikacji ruchu internetowego pod kątem różnorodności w zastosowanych metodach. Prace zostały wybrane z najciekawszych naszym zdaniem publikacji z ostatnich kilku lat (2009-2012). W porównaniu do istniejących przeglądów literaturowych - np. [13], [14], czy [3] - niniejszy artykuł dotyczy nowszych badań, oraz wykazuje, że łączenie wielu metod klasyfikacji w jeden system może być ciekawym kierunkiem dla przyszłych badań w tej dziedzinie. Klasyfikacja ruchu internetowego polega na odgadnięciu nazwy protokołu komunikacyjnego lub aplikacji, która wygenerowała dany ciąg pakietów IR Informacja ta jest przydatna np. w zarządzaniu ruchem w sieciach internetowych, gdy potrzeba kształtować ruch w zależności od jego rodzaju. Klasyfikacja ruchu znajduje zastosowanie także w zagadnieniach sieciowych związanych z wdrażaniem zasad bezpieczeństwa (np. zakaz stosowania aplikacji Skype), monitorowaniem natężenia ruchu (np. wykrywanie ataków DoS), oraz wielu innych. Przegląd literatury został podzielony na 4 kategorie: klasyfikacja ruchu (rozdział 3.1., prace nr 16), detekcja pojedynczych aplikacji (rozdział 3.2., prace nr 7-8), metody pozyskiwania „wiedzy bazowej" (ang. ground truth, rozdział 3.3., prace nr 9-11), oraz inne (rozdział 3.4., prace nr 12 i 13). Wszystkie prace zostały podsumowane w Tabeli 3. W ostatnim rozdziale (str. 10) prezentujemy wyniki przeglądu. Pokazujemy na przykład, że istnieje wiele metod klasyfikacji, które mogą być połączone w jeden system i wzajemnie się uzupełniać - przez multiklasyfikację (ang. multi-classification] lub obsługę różnych części ruchu (np. [31] dla TCP i [15] dla UDP). Podajemy także nasze rekomendacje dotyczące walidacji metod klasyfikacji i zbierania śladów ruchu internetowego.
Źródło:
Theoretical and Applied Informatics; 2013, 25, 2; 119-136
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Development of Blood Glucose Monitoring System using Image Processing and Machine Learning Techniques
Autorzy:
Thomas, Angel
Palekar, Sangeeta
Kalambe, Jayu
Powiązania:
https://bibliotekanauki.pl/articles/2077647.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
glucose
image processing
machine learning
colorimetry
Opis:
Glucose concentration measurement is essential for diagnosis, monitoring and treatment of various medical conditions like diabetes mellitus, hypoglycemia, etc. This paper presents a novel image-processing and machine learning based approach for glucose concentration measurement. Experimentation based on Glucose oxidase - peroxidase (GOD/POD) method has been performed to create the database. Glucose in the sample reacts with the reagent wherein the concentration of glucose is detected using colorimetric principle. Colour intensity thus produced, is proportional to the glucose concentration and varies at different levels. Existing clinical chemistry analyzers use spectrophotometry to estimate the glucose level of the sample. Instead, this developed system uses simplified hardware arrangement and estimates glucose concentration by capturing the image of the sample. After further processing, its Saturation (S) and Luminance (Y) values are extracted from the captured image. Linear regression based machine learning algorithm is used for training the dataset consists of saturation and luminance values of images at different concentration levels. Integration of machine learning provides the benefit of improved accuracy and predictability in determining glucose level. The detection of glucose concentrations in the range of 10–400 mg/dl has been evaluated. The results of the developed system were verified with the currently used spectrophotometry based Trace40 clinical chemistry analyzer. The deviation of the estimated values from the actual values was found to be around 2-3%.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 323--328
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Adaptive controller design for electric drive with variable parameters by Reinforcement Learning method
Autorzy:
Pajchrowski, T.
Siwek, P.
Wójcik, A.
Powiązania:
https://bibliotekanauki.pl/articles/201068.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
Reinforcement Learning
adaptive control
electric drive
machine learning
Opis:
The paper presents a method for designing a neural speed controller with use of Reinforcement Learning method. The controlled object is an electric drive with a synchronous motor with permanent magnets, having a complex mechanical structure and changeable parameters. Several research cases of the control system with a neural controller are presented, focusing on the change of object parameters. Also, the influence of the system critic behaviour is researched, where the critic is a function of control error and energy cost. It ensures long term performance stability without the need of switching off the adaptation algorithm. Numerous simulation tests were carried out and confirmed on a real stand.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 5; 1019-1030
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Ensemble of Statistical Metadata and CNN Classification of Class Imbalanced Skin Lesion Data
Autorzy:
Nayak, Sachin
Vincent, Shweta
Sumathi, K.
Kumar, Om Prakash
Pathan, Sameena
Powiązania:
https://bibliotekanauki.pl/articles/2055258.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classification
Convolutional Neural Networks
Ensemble Learning
machine learning
metadata
Opis:
Skin Cancer is one of the most widely present forms of cancer. The correct classification of skin lesions as malignant or benign is a complex process that has to be undertaken by experienced specialists. Another major issue of the class imbalance of data causes a bias in the results of classification. This article presents a novel approach to the usage of metadata of skin lesions images to classify them. The usage of techniques addresses the problem of class imbalance to nullify the imbalances. Further, the use of a convolutional neural network (CNN) is proposed to finetune the skin lesion data classification. Ultimately, it is proven that an ensemble of statistical metadata analysis and CNN usage would result in the highest accuracy of skin color classification instead of using the two techniques separately.
Źródło:
International Journal of Electronics and Telecommunications; 2022, 68, 2; 251--257
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
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:
Stationary supercapacitor energy storage operation algorithm based on neural network learning system
Autorzy:
Jefimowski, W.
Nikitenko, A.
Drążek, Z.
Wieczorek, M.
Powiązania:
https://bibliotekanauki.pl/articles/200935.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
stationary energy storage
operation algorithms
machine learning
supervised learning
prediction
Opis:
The paper proposes to apply an algorithm for predicting the minimum level of the state of charge (SoC) of stationary supercapacitor energy storage system operating in a DC traction substation, and for changing it over time. This is done to insure maximum energy recovery for trains while braking. The model of a supercapacitor energy storage system, its algorithms of operation and prediction of the minimum state of charge are described in detail; the main formulae, graphs and results of simulation are also provided. It is proposed to divide the SoC curve into equal periods of time during which the minimum states of charge remain constant. To predict the SoC level for the subsequent period, the learning algorithm based on the neural network could be used. Then, the minimum SoC could be based on two basic types of data: the first one is the time profile of the energy storage load during the previous period with the constant minimum SoC retained, while the second one relies on the trains’ locations and speed values in the previous period. It is proved that the use of variable minimum SoC ensures an increase of the energy volume recovered by approximately 10%. Optimum architecture and activation function of the neural network are also found.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 4; 733-738
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
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
Artykuł
Tytuł:
Prediction of PM2.5 hourly concentrations in Beijing based on machine learning algorithm and ground-based LiDAR
Autorzy:
Fang, Zhiyuan
Yang, Hao
Li, Cheng
Cheng, Liangliang
Zhao, Ming
Xie, Chenbo
Powiązania:
https://bibliotekanauki.pl/articles/2073773.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
PM2.5
LiDAR
machine learning
air pollution monitoring
Opis:
The prediction of PM2.5 is important for environmental forecasting and air pollution control. In this study, four machine learning methods, ground-based LiDAR data and meteorological data were used to predict the ground-level PM2.5 concentrations in Beijing. Among the four methods, the random forest (RF) method was the most effective in predicting ground-level PM2.5 concentrations. Compared with BP neural network, support vector machine (SVM), and various linear fitting methods, the accuracy of the RF method was superior by 10%. The method can describe the spatial and temporal variation in PM2.5 concentrations under different meteorological conditions, with low root mean square error (RMSE) and mean square deviation (MD), and the consistency index (IA) reached 99.69%. Under different weather conditions, the hourly variation in PM2.5 concentrations has a good descriptive ability. In this paper, we analyzed the weights of input variables in the RF method, constructed a pollution case to correspond to the relationship between input variables and PM2.5, and analyzed the sources of pollutants via HYSPLIT backward trajectory. This method can study the interaction between PM2.5 and air pollution variables, and provide new ideas for preventing and forecasting air pollution.
Źródło:
Archives of Environmental Protection; 2021, 47, 3; 98--107
2083-4772
2083-4810
Pojawia się w:
Archives of Environmental Protection
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Teaching Machines on Snoring : A Benchmark on Computer Audition for Snore Sound Excitation Localisation
Autorzy:
Qian, K.
Janott, C.
Zhang, Z.
Deng, J.
Baird, A.
Heiser, C.
Hohenhorst, W.
Herzog, M.
Hemmert, W.
Schuller, B.
Powiązania:
https://bibliotekanauki.pl/articles/177964.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
snore sound
obstructive sleep apnea
acoustic features
machine learning
Opis:
This paper proposes a comprehensive study on machine listening for localisation of snore sound excitation. Here we investigate the effects of varied frame sizes, and overlap of the analysed audio chunk for extracting low-level descriptors. In addition, we explore the performance of each kind of feature when it is fed into varied classifier models, including support vector machines, k-nearest neighbours, linear discriminant analysis, random forests, extreme learning machines, kernel-based extreme learning machines, multilayer perceptrons, and deep neural networks. Experimental results demonstrate that, wavelet packet transform energy can outperform most other features. A deep neural network trained with subband Energy ratios reaches the highest performance achieving an unweighted average recall of 72.8% from four types for snoring.
Źródło:
Archives of Acoustics; 2018, 43, 3; 465-475
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control
Autorzy:
Wodziński, M.
Krzyżanowska, A.
Powiązania:
https://bibliotekanauki.pl/articles/221525.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
shortest path
sequential data
quadrocopter
GPU
CUDA
Opis:
This paper presents an alternative approach to the sequential data classification, based on traditional machine learning algorithms (neural networks, principal component analysis, multivariate Gaussian anomaly detector) and finding the shortest path in a directed acyclic graph, using A* algorithm with a regression-based heuristic. Palm gestures were used as an example of the sequential data and a quadrocopter was the controlled object. The study includes creation of a conceptual model and practical construction of a system using the GPU to ensure the realtime operation. The results present the classification accuracy of chosen gestures and comparison of the computation time between the CPU- and GPU-based solutions.
Źródło:
Metrology and Measurement Systems; 2017, 24, 2; 265-276
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Relationship Between the Implementation Levels of Industry 4.0 Technologies and Advanced Manufacturing Technologies
Autorzy:
Sari, Tuğba
Powiązania:
https://bibliotekanauki.pl/articles/2172185.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
advanced manufacturing technologies
AMTs
industry 4.0
machine learning
Opis:
Industry 4.0 is expected to provide high quality and customized products at lower costs by increasing efficiency, and hence create a competitive advantage in the manufacturing industry. As the emergence of Industry 4.0 is deeply rooted in the past industrial revolutions, Advanced Manufacturing Technologies of Industry 3.0 are the precursors of the latest Industry 4.0 technologies. This study aims to contribute to the understanding of technological evolution of manufacturing industry based on the relationship between the usage levels of Advanced Manufacturing Technologies and Industry 4.0 technologies. To this end, a survey was conducted with Turkish manufacturers to assess and compare their manufacturing technology usage levels. The survey data collected from 424 companies was analyzed by machine learning approach. The results of the study reveal that the implementation level of each Industry 4.0 technology is positively associated with the implementation levels of a set of Advanced Manufacturing Technologies.
Źródło:
Management and Production Engineering Review; 2022, 13, 3; 52--60
2080-8208
2082-1344
Pojawia się w:
Management and Production Engineering Review
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A New Proposal for Person Identication Based on the Dynamics of Typing : Preliminary Results
Autorzy:
Buza, K.
Neubrandt, D.
Powiązania:
https://bibliotekanauki.pl/articles/375865.pdf
Data publikacji:
2016
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
person identification
dynamic time warping
hubness-aware machine learning
Opis:
The availability of cheap and widely applicable person identification techniques is essential due to a wide-spread usage of online services. The dynamics of typing is characteristic to particular users, and users are hardly able to mimic the dynamics of typing of others. State-of-the-art solutions for person identification from the dynamics of typing are based on machine learning. The presence of hubs, i.e., few instances that appear as nearest neighbours of surprisingly many other instances, have been observed in various domains recently and hubness-aware machine learning approaches have been shown to work well in those domains. However, hubness has not been studied in the context of person identification yet, and hubnessaware techniques have not been applied to this task. In this paper, we examine hubness in typing data and propose to use ECkNN, a recent hubness-aware regression technique together with dynamic time warping for person identification. We collected time-series data describing the dynamics of typing and used it to evaluate our approach. Experimental results show that hubness-aware techniques outperform state-of-the-art time-series classifiers.
Źródło:
Theoretical and Applied Informatics; 2016, 28, 1-2; 1-12
1896-5334
Pojawia się w:
Theoretical and Applied Informatics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization
Autorzy:
Barszcz, Tomasz
Zabaryłło, Mateusz
Powiązania:
https://bibliotekanauki.pl/articles/2052104.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning
fault detection
transient
turbine generator
genetic algorithm
Opis:
Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.
Źródło:
Metrology and Measurement Systems; 2022, 29, 1; 175-190
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Review of Artificial Intelligence Algorithms in Document Classification
Autorzy:
Bilski, A.
Powiązania:
https://bibliotekanauki.pl/articles/226245.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
classifier
text classification
data mining
information retrieval
machine learning algorithms
Opis:
With the evolution of Internet, the meaning and accessibility of text documents and electronic information has increased. The automatic text categorization methods became essential in the information organization and data mining process. A proper classification of e-documents, various Internet information, blogs, emails and digital libraries requires application of data mining and machine learning algorithms to retrieve the desired data. The following paper describes the most important techniques and methodologies used for the text classification. Advantages and effectiveness of contemporary algorithms are compared and their most notable applications presented.
Źródło:
International Journal of Electronics and Telecommunications; 2011, 57, 3; 263-270
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods for optimal compatibility of materials in ecodesign
Autorzy:
Rojek, I.
Dostatni, E.
Powiązania:
https://bibliotekanauki.pl/articles/202203.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
machine learning methods
classification models
ecodesign
selection of materials
compatibility
Opis:
Machine learning (ML) methods facilitate automated data mining. The authors compare the effectiveness of selected ML methods (RBF networks, Kohonen networks, and random forest) as modelling tools supporting the selection of materials in ecodesign. Applied in the design process, ML methods help benefit from the knowledge, experience and creativity of designers stored in historical data in databases. Implemented into a decision support system, the knowledge can be utilized – in the case under analysis – in the process of design of environmentally friendly products. The study was initiated with an analysis of input data for the selection of materials. The input data, specified in cooperation with designers, include both technological and environmental parameters which guarantee the desired compatibility of materials. Next, models were developed using selected ML methods. The models were assessed and implemented into an expert system. The authors show which models best fit their purpose and why. Models supporting the selection of materials, connections and disassembly methods help boost the recycling properties of designed products.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2020, 68, 2; 199-206
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł

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