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International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE)

作者： Aigul Adamova Tamara Zhukabayeva Laula Zhumabayeva Zhanna Mukanova Khu Ven-Tsen Department of Computer engineering and software Saken Seifullin Kazakh Agrotechnical University Astana Kazakhstan Department of Computing Engineering L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Information Systems and Power Engineering S. Yessenov Caspian State University of Technology and Engineering Aktau Kazakhstan Department of Information and Computing Systems Karaganda Economic University of Kazpotrebsoyuz Karaganda Kazakhstan Department of Computer engineering and software programming M. Auezov South Kazakhstan State University Shymkent Kazakhstan

ISBN: (数字)9781538659953

ISBN: (纸本)9781538659960

Development of the mobile robot can be executed in two different ways. Firstly, this creation of the built-in system, secondly, the use of "ready to use" industrial components. With distribution of the industrial mobile robots in the market there are more and more components which can be used for creation of all control system and sensors of the robot mobile platform. Use of the ready components doesn't demand the hardware development that accelerates time of development and reduces cost. The chassis of the Belarus-132 mini-tractor is used as a platform of robotic system in the research, "creation" of the program for traffic control is required. Results of the practical research on design and model development of a mobile robotic complex are given.

关键词： Control systems Robot kinematics Mobile robots Navigation Robot sensing systems

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

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Communication, Computer Sciences and Engineering (IC3SE), International Conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.

关键词： Deep learning Decision support systems Analytical models Data analysis Automation Education Companies

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments 5

Scientific Data Lake for High Luminosity LHC project and oth...

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5th International Conference on Particle Physics and Astrophysics, ICPPA 2020

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Smirnov, A. Smirnov, S. Zarochentsev, A. Compiler Technology Department Ivannikov Institute for System Programming of the Ras Moscow109004 Russia Cloud Technologies and Big Data Analytics Laboratory Plekhanov Russian University of Economics Moscow117997 Russia Atlas Group Universidad Andres Bello Santiago7550196 Chile Department of Computing Resources and Technologies Nrc "kurchatov Institute"-PNPI Gatchina188300 Russia Physics Department Brookhaven National Laboratory UptonNY11973 United States Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg198504 Russia

The next phase of LHC Operations-High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-The-Art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Eigenvalue tunneling and decay of quenched random network

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Physical Review E 2016年第6期94卷 062313-062313页

作者： V. Avetisov M. Hovhannisyan A. Gorsky S. Nechaev M. Tamm O. Valba N. N. Semenov Institute of Chemical Physics of the Russian Academy of Sciences 119991 Moscow Russia Department of Applied Mathematics National Research University Higher School of Economics 101000 Moscow Russia Chair of Programming and Information Technologies Yerevan State University Yerevan Armenia Institute of Information Transmission Problems Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny 141700 Russia Poncelet Laboratory Centre National de la Recherche Scientifique (UMI2615) Independent University of Moscow Moscow Russia P. N. Lebedev Physical Institute Russian Academy of Sciences 119991 Moscow Russia Physics Department Moscow State University 119992 Moscow Russia

We consider the canonical ensemble of N-vertex Erdős-Rényi (ER) random topological graphs with quenched vertex degree, and with fugacity μ for each closed triple of bonds. We claim complete defragmentation of large-N graphs into the collection of [p−1] almost full subgraphs (cliques) above critical fugacity, μc, where p is the ER bond formation probability. Evolution of the spectral density, ρ(λ), of the adjacency matrix with increasing μ leads to the formation of a multizonal support for μ>μc. Eigenvalue tunneling from the central zone to the side one means formation of a new clique in the defragmentation process. The adjacency matrix of the network ground state has a block-diagonal form, where the number of vertices in blocks fluctuates around the mean value Np. The spectral density of the whole network in this regime has triangular shape. We interpret the phenomena from the viewpoint of the conventional random matrix model and speculate about possible physical applications.

关键词： Clustering Collective behavior in networks Critical phenomena Evolving networks Network evolution Network phase transitions

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Analysing the impact of COVID-19 outbreak and economic policy uncertainty on stock markets in major affected economies

Analysing the impact of COVID-19 outbreak and economic polic...

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6th Smart Cities Symposium (SCS 2022)

作者： A. Matuka S. S. Asafo G. O. Eweke P. Mishra S. Ray M. Abotaleb T. Makarovskikh A.A. Alhussan A.A. Abdelhamid E.M. El-Kenawy P. Dutta S. Chowdhury University of Macerata Department of Economics and Law Macerata Italy Department of Banking & Finance Niger Delta University Amassoma Nigeria College of Agriculture J.N.K.V.V. Powarkheda India Centurion University of Technology and Management Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog School of Engineering and Technology Amity University Kolkata Kolkata India MCA Department Sri Venkateshwara College of Engineering and Technology Chittoor India

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts.

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Recurrent Neural Networks on Drifting Time-of-Flight Measurements

Recurrent Neural Networks on Drifting Time-of-Flight Measure...

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International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Tobias Feigl Thorsten Nowak Michael Philippsen Thorsten Edelhäußer Christopher Mutschler Programming Systems Group (Informatics II) Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Institute of Information Technology (Comm. Electronics) Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Precise Positioning and Analytics Department Fraunhofer Institute for Integrated Circuits IIS Nürnberg Nürnberg Germany Machine Learning and Data Analytics Lab Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany

Kalman filters (KFs) are popular methods to estimate position information from a set of time-of-flight (ToF) values in radio frequency (RF)-based locating systems. Such filters are proven to be optimal under zero-mean Gaussian error distributions. In presence of multipath propagation ToF measurement errors drift due to small-scale motion. This results in changing phases of the multipath components (MPCs) which cause a drift on the ToF measurements. Thus, on a short-term scale the ToF measurements have a non-constant bias that changes while moving. KFs cannot distinguish between the drifting measurement errors and the true motion of the tracked object. Hence, very rigid motion models have to be used for the KF which commonly causes the filters to diverge. Therefore, the KF cannot resolve the short-term errors of consecutive measurements and the long-term motion of the tracked object. This paper presents a data-driven approach that uses training sequences to derive a near-optimal position estimator. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) learns to interpret drifting errors in ToF measurements of a tracked dynamic object directly from raw ToF data. Our evaluation shows that our approach outperforms state-of-the-art KFs on both synthetically generated and real-world dynamic motion trajectories that include drifting ToF measurement errors.

关键词： Trajectory Hidden Markov models Tracking Measurement uncertainty Neural networks Position measurement Dynamics

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Preface

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2021年 1221 AISC卷 v-vi页

作者： Balas, Valentina Emilia Jain, Lakhmi C. Balas, Marius Mircea Shahbazova, Shahnaz N. "Aurel Vlaicu" University of Arad Faculty of Engineering Arad Romania Faculty of Engineering and Information Technology University of Technology Sydney Centre for Artificial Intelligence Sydney Australia Faculty of Engineering "Aurel Vlaicu" University of Arad Arad Arad Romania Department of Information Technology and Programming Azerbaijan Technical University Baku Azerbaijan

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Formation of gas hydroxide of sulfur dioxide at liquid sulfur dioxide injection in a porous medium saturated with methane and water

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AIP Conference Proceedings 2019年第1期2141卷

作者： M. K. Khasanov S. R. Kildibaeva 1Department of Applied Informatics and Programming Faculty of mathematics and information technology Sterlitamak branch of Bashkir state University Lenin avenue 49 Sterlitamak 453100 Republic of Bashkortostan Russia

A mathematical model of injection of liquid sulfur dioxide into a porous medium saturated with water and methane, accompanied by the formation of SO2 gas hydrate on the frontal surface is presented. The dependences of the temperature and the coordinates for the formation front of sulfur dioxide gas hydrate on the pressure of the injection and the initial pressure of the layer are explored. It is established that at sufficiently high injection pressure values and the initial temperature of the layer, as well as the low values of the layer’s initial pressure, the temperature of the porous medium on the boundary of gas hydrate formation of sulfur dioxide may rise above the decomposition’s equilibrium temperature of the gas hydrate of sulfur dioxide. This corresponds to the appearance of a zone in a porous medium saturated with a mixture of water, sulfur dioxide and its gas hydrate, being in a state of phase equilibrium.

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset

A Horizontal Federated Learning Approach to IoT Malware Traf...

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International Conference on Advanced Communication technology (ICACT)

作者： Phuc Hao Do Tran Duc Le Vladimir Vishnevsky Aleksandr Berezkin Ruslan Kirichek The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State University of Telecommunications Russia University of Science and Technology - The University of Danang Da Nang Viet Nam Information Technology Faculty The University of Danang - University of Science and Technology Danang Vietnam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Department of Programming Engineering and Computer Science Bonch Bruevich Saint Petersburg State University of Telecommunications

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.

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Identification of hand motion using background subtraction method and extraction of image binary with backpropagation neural network on skeleton model

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Journal of Physics: Conference Series 2018年第1期978卷

作者： Fauziah E P Wibowo S Madenda Hustinawati Information Technology Doctoral Programme in Gunadarma University Lecturer in Faculty of Informatic and Communication Technology Nasional University Indonesia Microelectronic & Images Processing Studies Gunadarma University Professor in Gunadarma University Indonesia Head Laboratory of Application and Programming Gunadarma University

Capturing and recording motion in human is mostly done with the aim for sports, health, animation films, criminality, and robotic applications. In this study combined background subtraction and back propagation neural network. This purpose to produce, find similarity movement. The acquisition process using 8 MP resolution camera MP4 format, duration 48 seconds, 30frame/rate. video extracted produced 1444 pieces and results hand motion identification process. Phase of image processing performed is segmentation process, feature extraction, identification. Segmentation using bakground subtraction, extracted feature basically used to distinguish between one object to another object. Feature extraction performed by using motion based morfology analysis based on 7 invariant moment producing four different classes motion: no object, hand down, hand-to-side and hands-up. Identification process used to recognize of hand movement using seven inputs. Testing and training with a variety of parameters tested, it appears that architecture provides the highest accuracy in one hundred hidden neural network. The architecture is used propagate the input value of the system implementation process into the user interface. The result of the identification of the type of the human movement has been clone to produce the highest acuracy of 98.5447%. The training process is done to get the best results.

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