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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Wasinski, Frank Bonath, Werner Ricklefs, Ubbo Borcsok, Josef Schwarz, Michael Hahn, Eike University of Applied Sciences THM Dept. of Electrical Engineering and Information Technology Giessen Germany Institute for Computer Architecture and System Programming University of Kassel ICAS Kassel Germany

ISBN: (纸本)9798350399714

Optical speed sensors based on the spatial frequency filter method are a proven technology that offer high measurement accuracy over a wide speed range. Newly developed variants also enable measurements at very low speeds and increase the robustness of the method [1]. This makes the sensor very suitable for use in the area of functional safety. A measurement chain up to safety integrity level 3 (SIL3) should be feasible via a combination with a safety system on a chip (SoC) for evaluating the measurement data and an extension of the sensor module to include safety-related elements [2], [3]. By integrating the sensor at IC level, a compact overall solution is to be made possible. After the first successful tests with the safety SoC, further examinations of the measuring method with regard to functional safety are now necessary. © 2023 IEEE.

关键词： System-on-chip

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Method of Assessing the Impact of External Factors on Geopositioning System Operation Using Android GPS API 16

Method of Assessing the Impact of External Factors on Geopos...

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16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Hovorushchenko, Tetiana Pavlova, Olga Avsiyevych, Volodymyr Computer Engineering and System Programming Department Khmelnytskyi Ukraine

ISBN: (纸本)9781665442572

The main methods of analysis the accuracy of the geolocation system were studied. The aspects of technology for obtaining geographical coordinates using Google geolocation system (GPS) were considered. The best method of obtaining data from satellites was searched. Today the geolocation system has become widely used in many industries, such as military, aviation, maritime, navigation of ordinary users. Also, a significant spread of this technology is noticed in many geographical areas, such as cartography, geodesy and is an area of interests of many scientists. A mobile app based on Android platform for conducting the experiments to determine the accuracy of geolocation services in the form of Web Quests was developed. © 2021 IEEE.

关键词： Global positioning system

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Advantages of Fog Computing: A Comparative Analysis with Cloud Computing for Enhanced Edge Computing Capabilities 4

Advantages of Fog Computing: A Comparative Analysis with Clo...

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

作者： Hunko, Mykhailo Tkachov, Vitelii Kuchuk, Heorhii Kovalenko, Andny Department of Electronic Computers Kharkiv National University of Radio Electonics Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

Fog computing extends the capabilities of cloud computing by enabling computing at the edge of the network, involving devices such as mobile collaborative devices or fixed nodes with integrated storage, computing, and communication capabilities. Fog computing offers benefits such as improved efficiency, increased security, savings in network bandwidth, and increased flexibility/in order to provide a complete understanding of Fog computing, this paper presents its salient features and highlights the differences from cloud computing research. Cloud computing is an emerging technology that offers computing resources on a pay-per-use basis. It provides three service models, and the cloud offers cost-effective centrally managed resources for reliable computing for specific tasks. This document presents a comparison between Fog computing and cloud computing, highlighting the differences in design, deployment, services, and tools available to organizations and users. By explicating the distinctiveness of fog computing and its contrast to cloud computing, we contribute to the field by shedding light on the unparalleled potential of fog computing in conserving resources while delivering robust computational solutions at the network periphery. Our study offers a new perspective that challenges the prevailing practices and extends the discourse on optimizing distributed computing architectures. © 2023 IEEE.

关键词： Fog computing

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences 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 Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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Optimization of Neural Network Calculations Using Integer Arithmetic

Optimization of Neural Network Calculations Using Integer Ar...

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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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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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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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Pricing Policy with the Effect of Fairness Concern, Imprecise Greenness, and Prices in Imprecise Market for a Dual Channel

Pricing Policy with the Effect of Fairness Concern, Imprecis...

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International Conference on Nonlinear Applied Analysis and Optimization, ICNAAO-2021

作者： Ganguly, Sanchari Das, Pritha Maiti, Manoranjan Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur WB Howrah711103 India Department of Applied Mathematics Oceanology and Computer Programming Vidyasagar University WB Midnapore721102 India

ISBN: (纸本)9789819905966

Fairness concern behavior, a well-known cognitive bias, refers to a person’s attitude of dissatisfaction for unequal pay-offs in someone’s favor. Against environmental pollution, many firms are focused on green manufacturing to maintain sustainable development. The adoption of online shopping by customers around the globe has altered the dynamics of competitiveness in the retail supply chain (SC). Considering the above facts, the effect of fairness concern on optimal prices and channel members’ profits in a two-level dual channel SC is investigated. Here, we consider an SC comprising a green manufacturer and a fair-minded retailer where the manufacturer produces and sells the green product through both offline and online channels to consumers. Due to uncertainty in the real world, the fuzziness is associated with market demand, price elasticity, and the coefficient of greenness. The fuzzy objectives and constraints are reduced to crisp ones using expectation and possibility measures, respectively. Both centralized and decentralized models (with and without cognitive bias) are formulated and solved by the Stackelberg game for the optimal prices and product greenness level. The models’ optimal solutions are analyzed and compared with the deterministic models numerically. The effects of the fairness concern coefficient on the optimal prices, product greenness level, and channel members’ profits are investigated. The sensitivity analyses are presented to study the effects of the fuzzy degree of customers’ sensitivity toward greenness on channel members’ profits. Retailer’s fairness is harmful to the manufacturer but beneficial for her own profit. Finally, some conclusions and managerial insights are presented. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Game theory

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Using LSTM as Intelligent Machine Learning Method to Forecast the Annual Average Relative Humidity: A Case Study for Konya, Turkiye

Using LSTM as Intelligent Machine Learning Method to Forecas...

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Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference

作者： Mayanja, Abubakar Eryürük, Şule Eryürük, Kağan Graduate School in Science Department of Computer Engineering Selcuk University Konya Turkey Vocational School of Commerce and Industry Department of Computer Programming KTO Karatay University Konya Turkey Department of Industrial Engineering KTO Karatay University Konya Turkey Department of Civil Engineering Necmettin Erbakan University Konya Turkey

ISBN: (纸本)9783031397769

Nowadays, one of the most demanding areas of Artificial Intelligence application is to forecast the weather condition as rapid and accurate as possible. The ratio of air vapor pressure to saturation vapor pressure is defined as relative humidity which is represented by a percentage of how much moisture the atmosphere can hold at the same temperature and pressure. The relative humidity has a significant impact on climate change and global warming which have had a negative impact on agriculture, economy, food security, and public health. In addition, accurate weather information and humidity forecasts are frequently required to warn of natural disasters result from unexpected changes in climatic conditions such as relative humidity. Therefore, recent decades have seen a rise in interest in this field of study. The aim of this study is to use Long Short-Term Memory (LSTM) models to forecast the annual relative humidity in Konya, Turkey between 1931 and 2010 by utilizing the annual average amounts of rain, sunlight, wind speed, and temperature. This paper represents a novel approach to predict annual relative humidity in Konya, which has not been previously explored in this location and between the given dates. As a result, the findings were able to forecast the monthly average humidity for next five years in advance with a mean square error of 0.1705% within 150 episodes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Application of IoT in the study of biogas release parameters 33

Application of IoT in the study of biogas release parameters

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33rd International Scientific Conference Electronics, ET 2024

作者： Nikolov, Neven K. Tzanova, Anelia I. Koparanov, Kiril A. Antonova, Elena V. Minkovska, Daniela V. Technical University of Sofia Faculty of Computer Systems and Technologies Department of Programming and Computer Technologies 8 Kliment Ohridski blvd. Sofia1000 Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Chemistry 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9798350376449

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 performed. Two types of samples were tested, the obtained results were displayed graphically, and the data was analyzed. © 2024 IEEE.

关键词： biogas ESP32 IoT measurement MQTT

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