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Sensing Metric-Based Hybrid Underlay/Overlay Transmission Framework for CR-IoT Networks for Smart City Applications 7

Sensing Metric-Based Hybrid Underlay/Overlay Transmission Fr...

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results. © The Institution of engineering & Technology 2023.

关键词： Smart city

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Optimization of Neural Network Calculations Using Integer Arithmetic

Optimization of Neural Network Calculations Using Integer Ar...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Oleksandr Zakovorotnyi Andrii Khulap Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

Most of the sensor devices in the Internet of Things systems are based on energy-efficient microcontrollers, the computing resources of which are limited, as well as the amount of available memory. Increasing the security of the use of such devices with the help of neural networks is an important and urgent problem. The article describes the possibility of using artificial neural networks in small microcontrollers with limited resources. The purpose of this work is to check the possibility of calculating neural networks based on integer arithmetic to reduce the time of calculating a neural network and eliminate data normalization operations, as well as to evaluate the feasibility of using such neural networks in the field of security of the Internet of Things in comparison with traditional methods, such as black lists and white lists. The following results were obtained: when switching to integer arithmetic, compared to floating point, the accuracy of the result calculations is within the permissible error of neural network training, that is, it has not changed. Execution time decreased by $30-96 \%$ , depending on the architecture of the microcontroller. The program size is reduced by $22-48 \%$ , also depending on the microcontroller architecture. Conclusions: the possibility and expediency of using neural networks optimized for microcontrollers with limited resources was proved. This will increase the security of Internet of Things systems, especially against device authentication threats and intrusion detection. Prospects for further research are determined.

关键词： Training Microcontrollers Neural networks Memory management Intrusion detection Switches Security Internet of Things Optimization Arithmetic

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

引用

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection computer Network Intrusion Using Deep Neural Networks

Detection Computer Network Intrusion Using Deep Neural Netwo...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Vadym Poltoratskyi Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

In this work, the effectiveness of using classical machine learning methods and modern deep neural network models for intrusion detection in computer networks has been investigated. The purpose of this work is to develop a model for detecting intrusions into computer networks based on the Transformer model using tabular input data. In this work, the UNSW-NB15 dataset is used as the source data. This dataset contains information about normal network behaviour as well as during synthetic intrusions. Models for intrusion detection in computer networks based on machine learning methods were investigated: Decision Tree, KNN, Logistic Regression, SVM, Gradient Boosting, Random Forest. A method of converting tabular data into images was developed, which made it possible to build intrusion detection models based on Vision Transformer and Vision Transformer for small-size datasets on modern Transformer architecture. The research results showed that developed model based on Vision Transformer and Vision Transformer for small-size datasets improves the quality of identification, and eliminates the need for a preprocessing step such as dimensionality reduction.

关键词： Support vector machines computer vision Computational modeling Intrusion detection Artificial neural networks Forestry Transformers Data models computer networks Regression tree analysis

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Application-based spam detection with machine learning algorithms 27

Application-based spam detection with machine learning algor...

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27th International Conference on Information Society and University Studies, IVUS 2022

作者： Erbey, Ali Barışçı, Necaattin Uşak University Distance Education Vocational School Department of Computer Programming Uşak Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

We investigated the process of unsupervised generative learning and the structure of informative generative representations of images of handwritten digits (MNIST dataset). Learning models with the architecture of sparse convolutional autoencoder with constraints to produce low-dimensional representations achieved successful generative learning demonstrated by high accuracy of generation of images. A well-defined, continuous and connected structure of generative representations was observed and described. Structured informative representations of unsupervised generative models can be an effective platform for investigation of origins of intelligent behaviors in artificial and biological learning systems. © 2022 Copyright for this paper by its authors.

关键词： Neural networks

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Heterogeneous Ensemble Classifier in Medical Decision Support Systems

Heterogeneous Ensemble Classifier in Medical Decision Suppor...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Anatol y Povoroznyuk Oksana Povoroznyuk Anna Filatova Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The work is devoted to solving the current scientific and technical problem of constructing a diagnostic decision support system in medicine based on a heterogeneous ensemble classifier model that implements two approaches to formulating a diagnostic conclusion: a probabilistic one based on the analysis of the training sample, and expert information on the structure of symptom complexes. The choice of prototype matching method as a probabilistic component is justified. Formalization of expert information on the structure of symptom complexes was carried out by representing symptom complexes of diseases with numerical intervals of linguistic variables. Options for taking into account expert assessments about the structure of symptom complexes in an ensemble classifier are considered. Test verification of the developed classifier was done on real medical data and confirmed the effectiveness of its work.

关键词： Decision support systems Training Analytical models Prototypes Linguistics Probabilistic logic Medical diagnostic imaging Diseases

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Modeling of DC Traction Motor In Ansys RMXPRT 4

Modeling of DC Traction Motor In Ansys RMXPRT

引用

4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Herasymenko, Vitalii Zakovorotnyi, Oleksandr Pliuhin, Vladyslav Mezentsev, Mykola Noskov, Valentyn Orlova, Tetyana Department of Electric Transport O. M. Beketov National University of Urban Economy in Kharkiv Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Department of the Systems of Electricity Supply and Electricity Consumption of Cities O. M. Beketov National University of Urban Economy in Kharkiv Kharkiv Ukraine Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine

ISBN: (纸本)9798350395532

DC traction motors are widely used in all branches of urban economy. They began to be used in industry about a hundred years ago with the advent of the first calculation methods. When designing a DC motor, it is important to consider it comprehensively, together with the electric drive system. The article discusses the application of a more correct approach to modeling a series-excited DC motor using the ANSYS software package, in particular, the ANSYS RMxprt/Maxwell/Simplorer bundle. The main stages of initial data input are considered and the main calculation results are shown. Using ANSYS Maxwell allows you to quickly and automatically build a typical machine geometry using RMxprt. When solving non-standard structures, it is possible to use 3D design with step-by-step elements. The analysis of the obtained graphic dependences of electric power parameters makes it possible to judge the efficiency of the electric motor and its operational properties in idling and under load. The simulation results show excellent agreement with the data obtained experimentally. © 2023 IEEE.

关键词： Efficiency

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Academic Fraud Detection in Online Exams with DNN's Multilayer Model

Academic Fraud Detection in Online Exams with DNN's Multilay...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Bahaddin Erdem Murat Karabatak Department of Computer Programming Bitlis Eren University Bitlis Turkey Department of Software Engineering Firat University Elaziğ Turkey

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This study aims to reveal the best deep learning models that are improved and optimized by predicting undesirable behavior patterns using a dataset consisting of artificial and real exam data of students taking online distance education courses in an online environment through the distance education system. Using online exam data of 129 students, the researchers conducted analysis with two different scenarios to determine the best prediction performance through regression and classification models. The model we proposed was determined as a four-layer DNN with 80.4% test performance in detecting students who “cheated” from undesirable behavior patterns, which was performed with K-10, K-5 and K-3 cross-validation. The results prove that students' online distance education exam data can be easily applied to the DNN model. The models presented in the study provide a roadmap for educational institutions to evaluate their online examination practices and develop more effective strategies for academic honesty.

关键词： Deep learning Analytical models Digital forensics Predictive models Nonhomogeneous media Data models Educational courses Fraud Security

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Spam Detection With Fasttext Based Features

Spam Detection With Fasttext Based Features

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Innovations in Intelligent Systems and Applications Conference

作者： Talha Karadeniz Gül Tokdemir H. Hakan Maraş Department of Software Engineering Çankaya University Ankara Turkey Department of Computer Engineering Çankaya University Ankara Turkey Department of Computer Programming Çankaya University Ankara Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Fasttext is a powerful word representation method that creates word representations based on vectors of character n-grams. In this work, we propose a method that utilizes fasttext features for a novel feature engineering model for the spam detection problem. In the feature engineering method, the combination of average, mean of second derivative; mean peak and standard deviation of fasttext features are computed. Finally, tf-idf features are also considered for the modeling process. The success of each feature engineering technique is measured and reported. The combination of the five feature extraction methods, tested on two spam detection datasets, yielded promising results with an accuracy of 0.978 on e-mail spam detection and an accuracy of 0.986 on sms spam classification.

关键词： Technological innovation Accuracy Unsolicited e-mail Computational modeling Feature extraction Vectors Intelligent systems Standards Postal services

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Synthesis of The Theory of Motion of Solid Bodies Filled With Bulk Substances For Fault-Tolerant Identification of Their Parameters 13

Synthesis of The Theory of Motion of Solid Bodies Filled Wit...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Kolodnenko, Vitalii Shyman, Anna Kalinin, Yevhen Kuchuk, Nina Sumy National Agrarian University Department of Agricultural Engineering Sumy Ukraine National Technical University Kharkiv Polytechnic Institute Department of Computer Engineering and Programming Kharkiv Ukraine National University of life and environmental sciences of Ukraine Tractors Cars and Bioenergy Resources Department Kyiv Ukraine

ISBN: (纸本)9798350396119

In the article, the Lagrange equations of motion of a solid body having volumes fully or partially filled with a granular media presented in the form of an ideal liquid. To expand the possibility of applying the theory, it is assumed that the model bulk medium has a surface tension. The first integrals of these equations have been considered. Further, conditions under which there is equilibrium or stationary motion of a solid with a loose medium that are reduced to the conditions of extremality (stationarity) of the potential energy or the modified potential energy of the system are derived from the equations of motion. In practically interesting cases, the problem of the minimum change in potential energy is solved by studying the second variation of the latter, the conclusion of the expression of which is given. In the nonlinear formulation, the theorem on the instability of the equilibrium position of a body with a granular media is proved in the case when the potential energy of the system does not have a minimum in the equilibrium position. © 2023 IEEE.

关键词： Potential energy

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