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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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Comparative analysis of signal processing methods secondary surveillance radar

Comparative analysis of signal processing methods secondary ...

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International Conference on Radio Electronics & Info Communications (UkrMiCo)

作者： A.E. Goriushkina I.V. Svyd G.E. Zavolodko G.V. Maistrenko Department of Computer science and Programming National Technical University «KhPI» NTU «KhPI» Kharkiv Ukraine Dept. Radiotechnologies Information and Communication Systems Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Information Systems National Technical University «KhPI» NTU «KhPI» Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (数字)9781538652640

ISBN: (纸本)9781538652657

In the paper, based on a comparative analysis of the methods of processing time-interval codes used as secondary surveillance radar information signals consisting of a different sequence of performing joint decoding operations, selecting signals by duration and time position, it is shown that the most effective is the signal processing method in which first the signals are decoded, then the signals are selected in accordance with the duration and in the future with the selection according to the time position, which provides the lowest probability of a false alarm of the first kind.

关键词： Radar Interference Decoding Security Radar signal processing Surveillance

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CATALYTIC INCINERATION OF EMISSIONS FOR ENERGY CONSERVATION.

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Plant/operations progress 1983年第3期2卷 182-184页

作者： Kenson, Robert E. Met-Pro Corp. Harleysville Pa. 19438 He has over fifteen years' experience in chemical process engineering including petrochemicals fertilizers synfuels energy recovery and polution control processes. In his present position he is responsible for all research/development design and project management activities in pollution control and energy/resource conservation systems for Met-Pro's Systems Division. The author of numerous publications he has presented papers before national meetings of WWEMA APCA AIChE Catalysis Society ACS and AIChE. Ph.D. from Purdue University and an AB from Boston University. He is a member of AIChE APCA AMA and Sigma Xi. As a member of AIChE he has been active as session chairman national program coordinator programming committee member and task force member for the Environmental Division.

The article demonstrates that catalytic incineration is often economically attractive as an alternative to thermal incineration for petroleum and petrochemical process organic emissions. Even where the initial cost of the catalytic incinerator is greater than a comparable-performance thermal incinerator, the lower fuel costs of the catalytic incinerator pay this cost differential back very quickly (1/2 to 2 years). Catalytic incineration can not only be used for pollution control, but also for energy recovery from streams containing combustible gases.

关键词： PETROCHEMICAL PLANTS

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Hydrogeological-Mathematical Model of Formation and Management of Resources and Quality of Fresh Underground Water of the Karakalpak Artesian Basin

Hydrogeological-Mathematical Model of Formation and Manageme...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Seitnazarov, Kuanish Aytanov, Anvar Kojametov, Ermuhammed Asenbaev, Nurbek Nukus Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Faculty of 'Computer Engineering' Nukus Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer Systems Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Computer Technologies and Programming Tashkent Uzbekistan Nukus Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Security Nukus Uzbekistan

ISBN: (纸本)9781665432580

This article discusses the problems of using groundwater resources of the Karakalpak artesian basin. The task of drawing up a geological and mathematical model of the operational resources of groundwater in the Karakalpak artesian basin for the rational use of fresh groundwater in the basin is set. This article discusses the problem of lack of fresh groundwater in the territory of the Republic of Karakalpakstan. A method of hydrogeological-mathematical model of exploitable groundwater resources for the correct use of fresh groundwater is proposed. © 2021 IEEE.

关键词： Hydrogeology

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Forecasting Nonlinear Nonstationary Processes in Machine Learning Task

Forecasting Nonlinear Nonstationary Processes in Machine Lea...

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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Peter Bidyuk Aleksandr Gozhyj Irina Kalinina Victoria Vysotska Mikhail Vasilev Romanna Malets Department of Mathematical Methods of System Analysis National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Kyiv Ukraine Department of Intelligent Information Systems Petro Mohyla Black Sea National University Nikolaev Ukraine Department of Information Systems And Networks National University "Lvivska politechnika" Lviv Ukraine Programming Department Ivan Franko Lviv National University Lviv Ukraine

ISBN: (数字)9781728132143

ISBN: (纸本)9781728132150

The article discusses the features of the solving the forecasting problems using machine learning techniques. The issues of accounting and correctly processing non-linear non-stationary processes in the problems of modeling and forecasting time series in various areas are considered. The analysis of stages and methods for solving machine learning problems. As an example, consider the problem of predicting currency pairs based on historical data. A comparative analysis of the normalization methods in data clustering is given. For the six currency pairs a short-term forecast is proposed.

关键词： Forecasting Time series analysis Machine learning Load modeling Currencies Predictive models Classification algorithms

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Application of IoT in the study of biogas release parameters

Application of IoT in the study of biogas release parameters

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International Scientific Conference Electronics (ET)

作者： Neven K. Nikolov Anelia I. Tzanova Kiril A. Koparanov Elena V. Antonova Daniela V. Minkovska Department of Programming and Computer Technologies Faculty of Computer Systems and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria Department of Chemistry Faculty of Electronic Engineering and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia Bulgaria

This paper describes the development of an IoT device prototype for measuring biogas release parameters. Through the device, measurements are made, and statistics are accumulated based on which calculations are perfor... 详细信息

ISBN: (数字)9798350376449

ISBN: (纸本)9798350376456

关键词： Performance evaluation Heating systems Prototypes Production Sensor phenomena and characterization Biogas Sensor systems Internet of Things Information technology Monitoring

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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Computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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A Language for the Aid of Financial Fact Finders

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Financial Analysts Journal 1967年第1期23卷 121-129页

作者： Richard S. Bower Christopher E. Nugent J. Peter Williamson Barbara C. Myers Associate Professor of Business Economics Amos Tuck School of Business Administration Dartmouth College. Assistant Professor Business Administration Amos Tuck School of Business Administration Dartmouth College. Professor of Business Administration Amos Tuck School of Business Administration Dartmouth College. Senior Consultant Amos Flick School on Systems Programming and Computer Applications

The Time-Sharing Computer System at Dartmouth College enables users to retrieve and manipulate financial information by means of the LAFFF1 language. The first language actually in operation, it permits security analysts to engage in conversational exchange with the computers from distant stations. Analysts need no standard calculations; they can get what they want directly from the computer.

关键词： Time sharing Personal computers Price earnings ratio Financial advisers Stock prices Sales growth Dividends Teletype Run time

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Real-Time Gait Reconstruction For Virtual Reality Using a Single Sensor

Real-Time Gait Reconstruction For Virtual Reality Using a Si...

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Tobias Feigl Lisa Gruner Christopher Mutschler Daniel Roth Fraunhofer Institute of Integrated Circuits (IIS) Nürnberg Programming Systems Group Friedrich-Alexander University (FAU) Erlangen-Nürnberg Ludwig-MaximiliansUniversity (LMU) Technical University Munich (TUM) Munich Chair for Computer Aided Medical Procedures and Augmented Reality Technical University Munich (TUM)

ISBN: (数字)9781728176758

ISBN: (纸本)9781728176765

Embodying users through avatars based on motion tracking and reconstruction is an ongoing challenge for VR application developers. High quality VR systems use full-body tracking or inverse kinematics to reconstruct the motion of the lower extremities and control the avatar animation. Mobile systems are limited to the motion sensing of head-mounted displays (HMDs) and typically cannot offer *** propose an approach to reconstruct gait motions from a single head-mounted accelerometer. We train our models to map head motions to corresponding ground truth gait phases. To reconstruct leg motion, the models predict gait phases to trigger equivalent synthetic animations. We designed four models: a threshold-based, a correlation-based, a Support Vector Machine (SVM) -based and a bidirectional long-term short-term memory (BLSTM) -based model. Our experiments show that, while the BLSTM approach is the most accurate, only the correlation approach runs on a mobile VR system in real time with sufficient accuracy. Our user study with 21 test subjects examined the effects of our approach on simulator sickness and showed significantly less negative effects on disorientation.

关键词： Support vector machines Solid modeling Tracking Avatars Animation Real-time systems Delays

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Social Engineering Attacks Detection Approach

Social Engineering Attacks Detection Approach

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Sergii Lysenko Volodymyr Vorobiov Oleksandr Bokhonko Piotr Gaj Oleg Savenko Jacek Wołoszyn Department of Computer Engineering and Information Systems Khmelnitsky National University Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Department of Computer Engineering and System Programming Khmelnitsky National University Khmelnitsky Ukraine Department of Informatics and Teleinformatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom Poland

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy.

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