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13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Msallam, Mohammed Majid Samet, Refik Ankara University Computer Engineering Department Ankara Turkey University of Technology Control and Systems Engineering Department Baghdad Iraq

ISBN: (纸本)9798350340891

The Light-Fidelity (Li-Fi) is a wireless communication technology that is light-based and can complete wireless fidelity (Wi-Fi) technologies for many applications. Li-Fi technology which uses light spectrum is a technology for solving the lack of radio spectrum at Wi-Fi. One of the main benefits of Li-Fi technology is the ability to communicate by light with a high level of safety. However, the signal of Li-Fi technology is the same as other signals that are susceptible to eavesdropping because no security system is infallible. The objective of this paper is to decrease the computational cost of encryption and decryption and keep the security of transformed data when transmitted via Li-Fi technology. This study proposes the advanced Rivest Cipher 4 (ARC4) algorithm that will be used in the physical layer of Li-Fi applications. The novelty will be in the pseudo-random generation algorithm (PRGA) which is part of Rivest Cipher 4 (RC4). The data will become an index of the s-array and get the value of that index in the s-array instead of using the XOR operation. The proposed ARC4 was evaluated by the National Institute of Science and Technology (NIST) test. The obtained results illustrate that ARC4 is more robust and effective than RC4 when the data is transformed by Li-Fi communication. © 2023 IEEE.

关键词： Wi-Fi

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim 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 for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Prediction of the Magnitude of Lightning Impulse Interference in an Electronic Device Using an Artificial Neural Network

Prediction of the Magnitude of Lightning Impulse Interferenc...

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2024 International Conference on Industrial engineering, Applications and Manufacturing, ICIEAM 2024

作者： Gizatullin, Zinnur Mubarakov, Ramis Kazan National Research Technical University Named After A.N. Tupolev-KAI Department For Automated Systems For Information Processing And Control Department For Computer-Aided Design Systems Kazan Russia Kazan National Research Technical University Named After A.N. Tupolev-KAI Department For Computer-Aided Design Systems Kazan Russia

ISBN: (纸本)9798350395013

Reliable operation of modern electronic devices is the basis for their effective use. Electromagnetic interference, such as lightning, creates the risk of temporary or permanent disruption of the functioning of device elements. Therefore, it is necessary to take into account the possible consequences of such interference on the basis of prediction even at the design stage of electronic devices. The paper a technique for predicting the magnitude of lightning impulse interference in electronic devices using an artificial neural network is proposed. The advantage of this approach over simulation modeling is that rather complex three-dimensional models and long computation times are not needed. A stand was developed and obtained by measuring the amount of lightning interference in an electronic device. The features of the task of training an artificial neural network based on experimental data are considered. The architecture and parameters of the artificial neural network have been selected. An artificial neural network was trained based on experimental data. The root mean square error between the predicted data and the average impulse interference voltage values on the training data does not exceed acceptable values. The results of predicting the magnitude of lightning interference in an electronic device are presented. If we consider further prospects for using an artificial neural network to predict electromagnetic interference, we can note the relevance of the problem of the impact of electrostatic discharge, intentional short pulse and etc. © 2024 IEEE.

关键词： Neural networks

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An Approach to the Definition of System Intelligence in the Management of Complex systems 26

An Approach to the Definition of System Intelligence in the ...

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26th International Conference on Soft Computing and Measurements, SCM 2023

作者： Simankov, Vladimir S. Onishchenko, Stefan V. Buchatskiy, Pavel Yu. Teploukhov, Semen V. Kuban State Technological University Department of Computer Technologies and Information Security Krasnodar Russia Adyghe State University Department of Automated Systems of Processing Information and Control Maykop Russia

ISBN: (纸本)9798350322484

Currently, the concept of artificial intelligence has become extremely widespread, but there is a difficulty in accurately defining this concept. In general, artificial intelligence is divided into two main categories: 'weak AI' and 'strong AI'.In parallel with these definitions, the concept of hybrid intelligent systems arises, which can be defined from two main positions: as systems based on a combination of two or more artificial intelligence methods, and as systems based on the synthesis of artificial intelligence methods and natural human *** this regard, this paper proposes to analyze approaches to the organization of artificial intelligence and formulate a definition of system intelligence, which allows combining various structures of the organization of computer systems using various intelligent agents. © 2023 IEEE.

关键词： Decision making

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DeepLeaf: an optimized deep learning approach for automated recognition of grapevine leaf diseases

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Neural Computing and Applications 2025年第15期37卷 8799-8823页

作者： Talaat, Fatma M. Shams, Mahmoud Y. Gamel, Samah A. ZainEldin, Hanaa Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Faculty of Engineering Horus University Damietta Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura Egypt

Plant diseases can cause severe losses in agricultural production, impacting food security and safety. Early detection of plant diseases is crucial to minimize crop damage and ensure agricultural sustainability. Manual monitoring is often impractical due to the complexity and time involved, making automated disease recognition essential. This study presents a new Plant Disease Detection Algorithm (PDDA) called DeepLeaf focused on identifying four common grapevine diseases: leaf blight, black rot, stable, and black measles. The PDDA integrates three key modules: an Image Preprocessing Module, a Feature Extraction Module, and an Optimized Convolutional Neural Network (OCNN)-based Classification Module. The OCNN forms the core of the classification system, with its hyperparameters fine-tuned using fuzzy optimization to enhance performance. Preprocessing techniques are applied to analyze diseased leaves, and a logistic regression algorithm is used to downsample the features for better analysis. The CNN is trained on images from the Plant Village dataset, allowing it to detect and classify grapevine leaf diseases accurately. The proposed model's efficiency in the automated diagnosis of grapevine diseases is demonstrated by its remarkable 99.7% accuracy rate. This high accuracy indicates that the PDDA may help with more effective and scalable plant disease monitoring, which will ultimately allow better agricultural practices. © The Author(s) 2025.

关键词： CNN Detection of leaf disease Extracting features Plant disease

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks 24

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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24th International Conference on control systems and computer Science, CSCS 2023

作者： Dediu, Viorel Udrea, Andreea Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Department of Automatic Control and Systems Engineering Bucharest Romania

ISBN: (纸本)9798350313390

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia. © 2023 IEEE.

关键词： Convolution

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Speech Recognition Using Spectrogram Images and Formant Data with Neural Networks 20th

Speech Recognition Using Spectrogram Images and Formant Dat...

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20th International Conference on Dependability of computer systems, DepCoS-RELCOMEX 2025

作者： Kucharski, Mateusz Skubalska-Rafajlowicz, Ewa Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wroclaw Poland Department of Computer Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031927331

This paper proposes a novel approach to improving speech recognition performance by combining spectrogram images with extracted formant frequency data within a dual input neural network framework. While spectrograms are widely used in speech recognition due to their ability to capture time, frequency, and amplitude information, they may not always effectively highlight the subtle characteristics of formant patterns, especially in low resolution images. To address this, we introduce a hybrid model that integrates spectrogram images with formant parameters derived using the linear predictive coding method. The model utilizes parallel convolutional neural network processing spectrogram images and the other handling time series formant data. Outputs are then concatenated and further processed through fully connected layers. This double channel design enables the network to take advantage of complementary information from both spectral and parametric speech features, leading to improved recognition accuracy. The model is trained and evaluated using the Google Speech Commands dataset, achieving superior results compared to traditional KNN classifiers, formant-only models, and standard spectrogram-based CNNs. The proposed method achieves a 93% recognition accuracy, outperforming conventional approaches and demonstrating the potential of formant-augmented neural networks for more robust speech recognition tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image enhancement

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Ontological Decision Support Model for Cargo Port Risk Management 7

Ontological Decision Support Model for Cargo Port Risk Manag...

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7th International Conference on Information, control, and Communication Technologies, ICCT 2023

作者： Bondareva, Irina Protalinskiy, Oleg Khanova, Anna Nesterova, Evgeniya Belova, Anastasiya Astrakhan State Technical University Department of Applied Informatics Astrakhan Russia Moscow Power Engineering Institute Department of Automated Control Systems of Thermal Processes Moscow Russia

ISBN: (纸本)9798350340945

Modern unstable conditions of the market environment dictate the need for transport logistics enterprises to predict possible risk situations in order to prevent them, as well as to develop tools that allow forming management decisions, evaluating the effectiveness of each of them and choosing the most appropriate one, taking into account specific tasks and various influencing factors. As risks, situations of failure to achieve the goals of the cargo port, as well as failure to achieve standard values by the indicators for assessing these goals are considered. Goals and indicators are indicated in the constructed balanced scorecard (BSC). A set of links between goals and indicators for their assessment with risks at various levels of management is presented in the form of a developed multi-level hybrid logical-probabilistic (LP) risk model of the cargo port, as well as in detailed scenario models of this LP model. The ontological model contains all the information about the interaction of elements that affect the onset of risky events, and allows you to select options for exiting a risky situation in accordance with various specified conditions. The simulation model allows, based on the results of requests to the ontological model and the scenarios of simulation experiments developed on their basis, to develop a management decision that allows avoiding risky situations in the longer term, taking into account all available stochastic characteristics and factors of influence. © 2023 IEEE.

关键词： Risk management

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Frequency Domain Auto-tuning of Structured LPV controllers for High-Precision Motion control 63

Frequency Domain Auto-tuning of Structured LPV Controllers f...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Broens, Yorick Butler, Hans Toth, Roland Asml Veldhoven Netherlands Eindhoven University of Technology Department of Electrical Engineering Eindhoven Netherlands Hunren Institute for Computer Science and Control Systems and Control Laboratory Hungary

ISBN: (纸本)9798350316339

Motion systems are a vital part of many industrial processes. However, meeting the increasingly stringent demands of these systems, especially concerning precision and throughput, requires novel control design methods that can go beyond the capabilities of traditional solutions. Traditional control methods often struggle with the complexity and position-dependent effects inherent in modern motion systems, leading to compromises in performance and a laborious task of controller design. This paper addresses these challenges by introducing a novel structured feedback control auto-tuning approach for multiple-input multiple-output (MIMO) motion systems. By leveraging frequency response function (FRF) estimates and the linear-parameter-varying (LPV) control framework, the proposed approach automates the controller design, while providing local stability and performance guarantees. Key innovations include norm-based magnitude optimization of the sensitivity functions, an automated stability check through a novel extended factorized Nyquist criterion, a modular structured MIMO LPV controller parameterization, and a controller discretization approach which preserves the continuous-time (CT) controller parameterization. The proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator prototype. © 2024 IEEE.

关键词： Continuous time systems

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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