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2020 International Russian Automation Conference, RusAutoCon 2020

作者： Kolesnikov, A.A. Kolesnikova, S.I. Institute of Computer Technology and Information Security South Federal University Taganrog Russia Institute of Computational Systems and Programming St.Petersburg State University of Aerospae Instrumentation St. Petersburg Russia

ISBN: (纸本)9781728161303

The nonlinear adaptation algorithm is considered as applied to stabilization of the center of mass of a moving object subject to nonrandom noise in the control variable. An unknown disturbance is a bounded and continuous function of time. To solve the problem of its modeling and suppression, the following result is used: continuous functions can be described as partial solutions of some differential equations. The developed control algorithm guarantees full compensation of the external disturbance under certain conditions. The simulation result of the obtained control is applied to a manipulation object. © 2020 IEEE.

关键词： Differential equations

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Prediction and graph visualization of cyber attacks using graph attention networks

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Computers & Security 2025年 157卷

作者： Mucahit Soylu Resul Das Inonu University Department of Organized Industrial Zone Vocational School Computer Programming Malatya Turkiye Firat University Faculty of Technology Department of Software Engineering 23119 Elazig Turkiye

This study proposes a hybrid approach for visualizing cyberattacks by combining the deep learning-based GAT model with JavaScript-based graph visualization tools. The model processes large, heterogeneous data from the UNSW-NB15 dataset to generate dynamic and meaningful graphs. In the data cleaning phase, missing and erroneous data were removed, unnecessary columns were discarded, and the data was transformed into a format suitable for modeling. Then, the data was converted into homogeneous graphs, and heterogeneous structures were created for analysis using the GAT model. GAT prioritizes relationships between nodes in the graph with an attention mechanism, effectively detecting attack patterns. The analyzed data was then converted into interactive graphs using tools like SigmaJS, with attacks between the same nodes grouped to reduce graph complexity. Users can explore these dynamic graphs in detail, examine attack types, and track events over time. This approach significantly benefits cybersecurity professionals, allowing them to better understand, track, and develop defense strategies against cyberattacks.

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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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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Comparative Study for Classification Methods to Predict and Detect Epilepsy Seizure 5

Comparative Study for Classification Methods to Predict and ...

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5th IEEE Middle East and Africa Conference on Biomedical Engineering, MECBME 2020

作者： El Halabi, Nashaat Abi Zeid Daou, Roy Achkar, Roger Hayek, Ali Boercsoek, Josef American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon American University of Science and Technology Department of Computer and Communications Engineering Faculty of Engineering Beirut Lebanon Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany Computer Science University of Kassel Department of Computer Architecture and System Programming Faculty of Electrical Engineering Germany

ISBN: (纸本)9781728123585

Epilepsy is a medical problem that tackles lots of patients. It limits the life activity of such patients due to the seizures that occur anytime and anywhere. Thus, creating a monitoring system that could make their life easier and allow them to perform daily life activities as safe as possible has been studied. This paper is a continuity for a project where a system monitors epileptic patients using a set of wearable sensors connected to several processing units, the main sensors are Electroencephalogram (EEG) for the electrical activity of the brain, electrocardiogram (ECG) for the electrical activity of the heart, and accelerometers to monitor the posture of the patient, features extracted from EEG and ECG signals are used for prediction/detection of seizures. Thus, the objective of this paper is to present a comparative study in order to choose the appropriate classification method for seizure prediction and detection. Five classification methods were studied: Decision Trees, Discriminant Analysis, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Learning. For each of the classification methods, the accuracy, the specificity, the sensitivity, the precision, the false detection rate and the Fmeasure were studied and compared. The results for seizure prediction system show that the highest accuracy, specificity and F-Measure, and lowest false detection rate were achieved with Bagged Trees Ensemble Learning, the highest sensitivity and precision were achieved with Quadratic Support Vector Machine. However, for seizure detection system, the highest accuracy and F-Measure were achieved with Linear Support Vector, the highest sensitivity and specificity were achieved with Fine Gaussian Support Vector Machine, and the highest precision and lowest false detection rate were achieved with Subspace Discriminant Ensemble Learning. © 2020 IEEE.

关键词： Support vector machines

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Intelligent System for Classifying of Acoustic Endoscopic Images of the Pancreas Based the Analysis of Walsh Spectra of Local Windows

Intelligent System for Classifying of Acoustic Endoscopic Im...

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Information technology (ACIT)

作者： Riad Taha Al-Kasasbeh Sergey Filist Olga Shatalova Dmitry Sergeevich Kondrashov Ashraf Shaqadan Nikolay A. Korenevskiy Osama M. Al-Habahbeh Emad Saleh Tarawneh Evgeny Starkov Mechatronics Engineering Department The University of Jordan Amman Jordan Department of SMART Technologies Moscow Polytechnic University Moscow Russian Federation Department of Biomedical Engineering Southwest State University Kursk Russian Federation Civil Engineering Department Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia South-West State University Russian Federation The University of Jordan Jordan Institute of System Programming of the Russian Academy of Sciences Russian Federation

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The purpose of the work is to develop and study methods and algorithms for intelligent classification systems for digital medical images. A method for automatic classification of medical images was developed using neural networks and transparency masks obtained by preliminary image segmentation. This allows you to generate descriptors only for those pixels that are selected by the transparency mask. Also algorithmic and software for classifying medical images has been developed. Experimental testing was carried out on ultrasound images of the pancreas. Differential diagnostics was carried out on control samples. The classes "oncology" and "pancreatitis" were divided. The diagnostic sensitivity by class turned out to be at least 0.8, while the diagnostic specificity is at least 0.85.

关键词： Image segmentation Ultrasonic imaging Sensitivity Neural networks Software algorithms Oncology Software Pancreas Intelligent systems Medical diagnostic imaging

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Chinese Phytopharmacology in dermatology - A Systematic Review

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Pharmacological Research - Modern Chinese Medicine 2023年 7卷

作者： Rodrigues, Jorge Magalhães Santos, Catarina Ribeiro, Victor Alvarenga, Anna Santos, Rosa Vilares IPTC – Research Department in Complementary Medicine of the Portuguese Institute of Taiji and Qigong Maia Portugal ABS – Health Level Atlântico Business School Vila Nova de Gaia Portugal FMUP - Faculty of Medicine of the University of Porto Portugal ProNutri: Clinical Nutrition & Disease Programming of the Centre for Health Technology and Services Research Porto Portugal

Background: Dermatologic diseases are one of the most frequent health conditions. The impact on the quality of life of patients is evident and the negative effects on health can range from psychological and physical incapacity to even death. Because of the serious impact that dermatologic diseases can have on one's health, prevention and treatment of these conditions should be urgently included in global health strategies. Advancing scientific knowledge about traditional techniques has been gaining acknowledgement due to the contributions they can provide to global health. Objective: This study aims to analyse the current evidence on the effectiveness of traditional Chinese phytopharmacology (herbal medicine) on dermatologic conditions and to inform potential applications within western healthcare settings. Methodology: A systematic literature search was conducted in PubMed, Cochrane, Science Direct and Scielo databases to identify randomized clinical trials on Chinese herbal medicine for dermatologic conditions. Studies with low or moderate quality were excluded. Results: Our literature search yielded 151 studies, 135 after the removal of duplicates. After title and abstract screening, 38 studies were selected for retrieval. Full-text and quality assessment was performed on 33 studies, and 18 studies were included in the final analysis. Conclusions: Evidence suggests that Chinese phytopharmacology can be used as a complementary treatment for some dermatologic conditions. Major benefits seem to exist due to the presence of components that possess anti-fungal, anti-bacterial, anti-viral, and anti-inflammatory action, as well as immunomodulatory effects. More studies should be conducted to confirm the complementary benefits of Chinese phytopharmacology for dermatologic conditions. Registration: The protocol has been registered with the International Prospective Registry for Systematic Reviews (PROSPERO CRD42022308304). © 2023

关键词： Chinese herbal medicine Dermatology traditional Chinese medicine

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Detection of variable misuse using static analysis combined with machine learning

Detection of variable misuse using static analysis combined ...

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2019 Ivannikov Ispras Open Conference, ISPRAS 2019

作者： Morgachev, Gleb Ignatyev, Valery Belevantsev, Andrey Ivannikov Institute for System Programming RAS Moscow Institute of Physics and Technology Moscow Russia

ISBN: (纸本)9781728160559

Industrial static analyzers are able to detect only several narrow classes of algorithmic errors, for example actual arguments order swapped with formal parameters, forgotten renaming of variable after copy-paste. However, even for these categories essential part of errors is lost because of heuristical design of a checker. We propose the generalization of specified errors in the form of variable misuse problem and deal with it using machine learning. The proposed method uses message propagation through the program model represented as a graph, combining data from multiple analysis levels, including AST, dataflow. We introduce several error criteria, which were evaluated on the set of open source projects with millions of LoC. Testing in close to industrial conditions shows good false positive and missed errors ratio comparable with remaining detectors and allows to include developed checker (after a minor rework) into a general purpose production static analyzer for error detection. © 2019 IEEE.

关键词： Error detection

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Finite groups whose n-maximal subgroups are σ-subnormal

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Science China Mathematics 2019年第7期62卷 1355-1372页

作者： Wenbin Guo Alexander N. Skiba School of Mathematical Sciences University of Science and Technology of China Hefei 230026 China Department of Mathematics and Technologies of Programming Francisk Skorina Gomel State University Gomel 246019 Belarus

Let σ={σi | i ∈ I} be some partition of the set of all primes P. A set H of subgroups of G is said to be a complete Hall σ-set of G if every member ≠ 1 of H is a Hall σi-subgroup of G, for some i ∈ I, and H contains exactly one Hall σi-subgroup of G for every σi ∈σ(G). A subgroup H of G is said to be:σ-permutable or σ-quasinormal in G if G possesses a complete Hall σ-set H such that HAx= AxH for all A ∈ H and x ∈ G:σ-subnormal in G if there is a subgroup chain A = A0≤A1≤···≤ At = G such that either Ai-1■Ai or Ai/(Ai-1)Ai is a finite σi-group for some σi ∈σ for all i = 1,..., *** Mn < Mn-1 <···< M1 < M0 = G, where Mi is a maximal subgroup of Mi-1, i = 1, 2,..., n, then Mn is said to be an n-maximal subgroup of G. If each n-maximal subgroup of G is σ-subnormal(σ-quasinormal,respectively) in G but, in the case n > 1, some(n-1)-maximal subgroup is not σ-subnormal(not σ-quasinormal,respectively) in G, we write mσ(G)= n(mσq(G)= n, respectively).In this paper, we show that the parameters mσ(G) and mσq(G) make possible to bound the σ-nilpotent length lσ(G)(see below the definitions of the terms employed), the rank r(G) and the number |π(G)| of all distinct primes dividing the order |G| of a finite soluble group G. We also give the conditions under which a finite group is σ-soluble or σ-nilpotent, and describe the structure of a finite soluble group G in the case when mσ(G)=|π(G)|. Some known results are generalized.

关键词： finite group n-maximal subgroup σ-subnormal subgroup σ-quasinormal subgroup σ-soluble group σ-nilpotent group

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Food resources in food system technology: Bifunctional food system technology based on pickering emulsions

Food resources in food system technology: Bifunctional food ...

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6th Smart Cities Symposium (SCS 2022)

作者： I. Potoroko A. Kadi A. Paymulina U. Bagale M. Abotaleb E. M. El-Kenawy Department of Food and Biotechnology South Ural State University Chelyabinsk Russia Siberian Federal Scientific Center of Agro-Bio Technologie Novosibirsk Russia Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt

The development of domestically generated food additives based on eco-technologies with specified functionaltechnological and bioactive qualities is now a significant focus of contemporary study. Pickering emulsions are more stable both physically and chemically, making them a highly promising method of introducing biologically active chemicals into the body. The purpose of this research was to determine if ultrasound could be used to create a bioactive colloidal Pickering emulsion system using flax cellulose or brown algal polysaccharides as a stabilizer. Pickering emulsions stabilized with fucoidan, sodium alginate, or flax cellulose, based on a lipid fraction of sunflower oil and cinnamon oil blend, or a lipid fraction of linseed oil, were applied to the subjects, who were then subjected to ultrasound treatment at 630W/l for 16 min (4 min on, 3 min off) at a controlled temperature of 50°C. The results obtained demonstrate the effectiveness of using ultrasonic exposure to create bioactive Pickering emulsion colloidal systems. Fucoidan stabilizes pickering emulsion, with the highest AOA values (5.120±0.005 DPPH, %) and a rise in AOA values in linseed oil-based emulsion samples from 30% to 100% in comparison to emulsion samples based on a blend of lipid phases. The viscoelastic characteristics of Pickering emulsions with various oil fractions vary dramatically for the integrated structure-forming components.

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