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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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Improved channel estimation for underwater wireless optical communication OFDM systems by combining deep learning and machine learning models

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Optical and Quantum electronics 2025年第3期57卷 1-21页

作者： Salama, Wessam M. Aly, Moustafa H. Department of Computer Engineering Faculty of Engineering Pharos University Canal El Mahmoudia Street Beside Green Plaza Complex 21648 Alexandria Egypt OSA Member Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Marine Transport Alexandria1029 Egypt

Recent studies on channel estimation in wireless communication systems have focused on deep learning methods. Our primary contribution is based on the use of DenseNet121 hybrid with Random Forest (RF), Gated Recurrent Units (GRU), Long Short-Term Memory Networks (LSTM), and Recurrent Neural Networks (RNN) to improve the channel estimation and lower the error rate. In order to mitigate inter-symbol interference and map the datasets, this paper introduces M-quadrature amplitude modulation (16-QAM) and orthogonal frequency division multiplexing (OFDM), which is based on quadrature phase shift keying (QPSK). Additionally, the existence or lack of cyclic prefixes forms the basis of our simulation. Additionally, the suggested models are investigated using pilot samples 2, 4, 8, and 64. Labeled OFDM signal samples, where the labels match the signal received after applying OFDM and passing through the medium, are used to train the proposed models. The DenseNet121 functions as a powerful feature extractor to extract intricate spatial information from received signal data. Sequential models like as RNN, LSTM, and GRU are used to model temporal dependencies in the retrieved features. RF is also utilized to exploit non-linear relationships and interactions between features to further increase prediction accuracy and reduce bit error rate (BER). By comparing the models using key metrics like accuracy, bit error rate (BER), and mean squared error (MSE), superior performance is attained based on the DenseNet121_RNN_GRU_RF model. Additionally, the DLMs are assessed against traditional methods like minimal mean square error (MMSE) and least squares (LS). Using the DenseNet121_RNN_GRU_RF model indicates a considerable gain over alternative architectures, with an improvement of 36.3% over DensNet121-RNN-LSTM-RF, according to a comparison of the suggested models without cyclic prefix for OFDM_QPSK. The improvement in percentages of roughly 63.3% over DensNet121-RNN-LSTM, 68.18% over De

关键词： Orthogonal frequency division multiplexing

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Analysis of Selected Electricity Quality Parameters in Low-Voltage Power Grids with Attached Photovoltaic Micro-Installations

Analysis of Selected Electricity Quality Parameters in Low-V...

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2023 Progress in Applied Electrical engineering, PAEE 2023

作者： Latka, Mlgorzata Faculty of Electrical and Computer Engineering Rzeszow University of Technology Department of Power Electronics and Power Engineering Rzeszow Poland

ISBN: (纸本)9798350316254

The article presents selected parameters of power quality in low-voltage power grids with connected photovoltaic micro-installations. The measurements and its analysis have been conducted for power quality evaluation due to frequent complaints made by recipients. Both recipients have been unsatisfied, those with photovoltaic panels installed and those attached to such power lines with PV panels. © 2023 IEEE.

关键词： Power quality

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Cloud Computing and Neutrosophic Logic: Navigating Uncertainty in Educational Administration

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Neutrosophic Sets and Systems 2025年 78卷 102-449页

作者： Yasser, Ibrahim Abd El-Khalek, Aya A. Khalid, Huda E. Salama, A.A. Essa, Ahmed K. Engineering Faculty University of Mansoura Mansoura35516 Egypt Communications and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura35524 Egypt Telafer University The Administration Assistant for the President of the Telafer University Telafer Iraq Department of Mathematics and Computer Science Faculty of Sciences Port Said University Port Said42526 Egypt

Educational institutions face inherent uncertainties in student performance, stakeholder priorities, and data analysis. This paper explores how cloud computing, with its data storage, analytics, and collaboration tools, can address these uncertainties in educational administration. We propose a neutrosophic framework utilizing "truth," "falsity," and "indeterminacy" values to represent these complexities within educational data. Real-world case studies analyze how cloud computing can reduce uncertainties and improve decision-making. Our findings reveal that cloud computing offers significant potential for navigating the "fog" of educational administration, but its effectiveness is enhanced by considering the "indeterminacy" inherent in educational data through the neutrosophic framework. This research contributes a unique neutrosophic perspective to the use of cloud computing for improved and more informed decision-making in educational administration. © 2025, University of New Mexico. All rights reserved.

关键词： Case Studies Cloud Computing Decision-Making Educational Administration Educational Data Neutrosophic Logic Uncertainty

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Hybrid Reptile-Snake Optimizer Based Channel Selection for Enhancing Alzheimer’s Disease Detection

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Journal of Bionic engineering 2025年第2期22卷 884-900页

作者： Digambar Puri Pramod Kachare Smith Khare Ibrahim Al-Shourbaji Abdoh Jabbari Abdalla Alameen Department of Electronics and Telecommunication Ramrao Adik Institute of TechnologyD.Y.Patil CampusNavi-MumbaiMaharashtra400706India Maersk Mc-Kinney Moller Institute Faculty of EngineeringUniversity of Southern DenmarkOdenseDenmark Department of Electrical and Electronics Engineering Jazan UniversityJazan45142Saudi Arabia Department of Computer Science University of HertfordshireHatfieldUK Department of Computer Engineering and Information College of Engineering in Wadi AlddawasirPrince Sattam Bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia

The global incidence of Alzheimer's Disease(AD)is on a swift *** Electroencephalogram(EEG)signals is an effective tool for the identification of AD and its initial Mild Cognitive Impairment(MCI)stage using machine learning *** of AD using EEG involves multi-channel ***,the use of multiple channels may impact the classification performance due to data redundancy and *** this work,a hybrid EEG channel selection is proposed using a combination of Reptile Search Algorithm and Snake Optimizer(RSO)for AD and MCI detection based on decomposition *** Mode Decomposition(EMD),Low-Complexity Orthogonal Wavelet Filter Banks(LCOWFB),Variational Mode Decomposition,and discrete-wavelet transform decomposition techniques have been employed for subbands-based EEG *** extracted thirty-four features from each subband of EEG ***,a hybrid RSO optimizer is compared with five individual metaheuristic algorithms for effective channel *** effectiveness of this model is assessed by two publicly accessible AD EEG *** accuracy of 99.22% was achieved for binary classification from RSO with EMD using 4(out of 16)EEG ***,the RSO with LCOWFBs obtained 89.68%the average accuracy for three-class classification using 7(out of 19)*** performance reveals that RSO performs better than individual Metaheuristic algorithms with 60%fewer channels and improved accuracy of 4%than existing AD detection techniques.

关键词： Alzheimer's Disease Brain disorder Electroencephalogram Reptile Search Algorithm Snake Optimizer Optimization

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua Science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Intelligent Farm Irrigation and Safety System 25

Intelligent Farm Irrigation and Safety System

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Smadi, Adnan Al-Nusairat, Murar Erjoub, Ammar Freitekh, Laith Khouj, Mohammed Zarqa Univeristy Faculty of Information Technology Department of Computer Science Zarqa Jordan Yarmouk University Engineering Faculty Depatrment of Electronics Engineering Irbid Jordan University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

In recent years, there has been growing interest in utilizing technology to improve agricultural practices and optimize crop yield. One area of focus is the development of intelligent farm irrigation systems that integrate automation, sensing, and control mechanisms to enhance the efficiency and effectiveness of farming operations. An important aspect of automated agriculture is the management of irrigation, as well as ensuring the safety of the farming environment. Plastic houses have become a cornerstone in modern agriculture, enabling controlled cultivation environments and increased yields. However, managing these environments efficiently requires a harmonious integration of various factors. This paper proposes an intelligent for irrigation, temperature, humidity, flame, gas detection, and fire alarm functions in a plastic house. The system presented here uses Arduino Microcontroller based system that integrates various sensors technology to monitor soil conditions accompanied by audible and visual alerts. Fire safety is ensured through flame and gas sensors, triggering immediate alerts. Python language was used to write the code for the Arduino microcontroller. © 2024 IEEE.

关键词： Smart agriculture

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ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi's Historical Insights

ThaiCoGen: Knowledge Graph Generation Enriching Suvarnabhumi...

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Takhom, Akkharawoot Sophaken, Chotanansub Vongpanich, Kantapong Utasri, Tharathon Saengthongpattana, Kanchana Supnithi, Thepchai Thammasat University Faculty Of Engineering Thammasat School Of Engineering Pathum Thani Thailand King Mongkut's University Of Technology Thonburi Faculty Of Engineering Bangkok Thailand National Electronics And Computer Technology Center Pathum Thani Thailand

ISBN: (纸本)9798350370096

Historical tourism documentation includes guidebooks, pamphlets, and digital resources, which are collections of materials that promote the exploration of historical sites, events, and cultural heritage. These materials aim to offer insights into the history and importance of particular locations, people, and items. Within these documents, nominal historical terminologies possess valuable information, serving as significant entities that can contribute to our understanding of history. This study introduces a method for knowledge graph (KG) generation through Thai co-occurrence network analysis, called ThaiCoGen. This work improves KG generation by using three important methods: (1) term frequency-inverse document frequency (TF-IDF) to find important terms and entities;(2) Thai co-occurrence network text analysis (ThaiCoNet) to connect semantic relationships;and (3) adding to KGs using linked open data from encyclopedias. Our contributions are the generation of KGs using extracted high-n-gram entities and the highlighting of semantic enrichment. Our contributions include (1) generating KGs by identifying the extracted height n-gram entities and (2) highlighting semantic enrichment. A case study was conducted on the Thai encyclopedia *** in the domain of the Suvarnabhumi Silk Road and demonstrated the efficacy of the ThaiCoGen methodology. Despite the substantial temporal gap between historical records from the Suvarnabhumi Silk Road era (100-700 BE) and contemporary tourist documentation from the modern era (2500 BE), ThaiCoGen successfully integrates these sources into enriched KGs. The discussion of this paper intends to elaborate on the historically accurate portrayal of the Suvarnabhumi Silk Road from the enriched KG, especially Thailand's tourism industry. Finally, this research provides the open KGs for Thailand's cultural heritage in order to build the robustness of semantic application development. © 2024 IEEE.

关键词： Knowledge graph

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ZnO Nanorods Coated Glass Substrate Based on Side Coupling Technique for LactoseSensing Application 4

ZnO Nanorods Coated Glass Substrate Based on Side Coupling T...

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4th International Conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Jahava, Marlina Jali, Mohd Hafiz Md Johari, Md Ashadi Norashikin, Mohd Adili Mohd Yusof, Haziezol Helmi Ahmad, Aminah Faculty of Electrical Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Electronics and Computer Technology and Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia Faculty of Information Technology and Computer Engineering Universiti Teknikal Malaysia Melaka Malacca Malaysia

ISBN: (纸本)9798350369694

A study has been conducted to investigate the sensing performance of zinc oxide (ZnO) nanorods coated glass substrate using the side coupling approach for lactose sensing application. The concentration of lactose solution was varied from 0% to 100% in order to examine the performance of the proposed sensor, which is influenced by the scattering and absorption of light. Coated glass substrates with optical side coupling could solve the lower output coupling voltage existed in the recent approach. Experimental results show that the proposed approach improved by a factor of 2.5 as compared to the uncoated glass substrate when exposed to variation concentration of lactose solution. The average sensitivity of the sensor was observed to be 0.0201 V/%Concentration throughout the tested %Concentration levels. The utilization of affordable and uncomplicated sensors allows for the precise identification of alterations in the refractive index solution, hence presenting potential applications in the domains of environmental and optical sensing. © 2023 IEEE.

关键词： Zinc oxide

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