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

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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A novel EO-based optimum random beamforming method in mmWave-NOMA systems with sparse antenna array

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Digital Communications and Networks 2024年第5期10卷 1313-1321页

作者： Fatemeh Asghari Azhiri Behzad Mozaffari Tazehkand Reza Abdolee Department of Electrical and Computer Engineering University of TabrizTabrizIran Department of Electrical and Computer Engineering California State UniversityBakersfieldUSA

Millimeter-wave(mmWave)Non-Orthogonal Multiple Access(NOMA)with random beamforming is a promising technology to guarantee massive connectivity and low latency transmissions of future generations of mobile *** this paper,we introduce a cost-effective and energy-efficient mmWave-NOMA system that exploits sparse antenna arrays in the *** analysis shows that utilizing low-weight and small-sized sparse antennas in the Base Station(BS)leads to better outage probability *** also introduce an optimum low complexity Equilibrium Optimization(EO)-based algorithm to further improve the outage *** simulation and analysis results show that the systems equipped with sparse antenna arrays making use of optimum beamforming vectors outperform the conventional systems with uniform linear arrays in terms of outage probability and sum rates.

关键词： Beamforming Millimeter-wave communication Non-orthogonal multiple access Sparse antenna arrays

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An analysis of parameters affecting ampacity in aircraft bipolar MVDC power cables via coupled electrical,thermal,and computational fluid dynamic modelling

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High Voltage 2024年第6期9卷 1208-1220页

作者： Arian Azizi Mona Ghassemi Zero Emission Realization of Optimized Energy Systems(ZEROES)LaboratoryDepartment of Electrical and Computer EngineeringThe University of Texas at DallasRichardsonTexasUSA

The next generation of aircraft,including more electric aircraft and all-electric aircraft(AEA),requires electric power systems with high power density and low system mass *** the voltage of the system to the range of a few kV,medium voltage(MV),is a reasonable approach to achieving high-power-density and low-system-mass EPSs for aircraft *** voltages,however,pose many challenges for aviation MV power cables such as arcs and arc tracking,partial discharges(PDs),and thermal *** this regard,thermal management is more challenging since heat transfer by convection is greatly reduced at wide-body aircraft's cruising altitudes due to the reduced air *** this paper,a finite element method(FEM)model is devel-oped in COMSOL Multiphysics for an aircraft bipolar MVDC(5 kV)power *** the model,the maximum permissible cable current at a low pressure of 18.8 kPa(at an altitude of 12.2 km from sea level,the usual cruising altitude for wide-body aircraft)is ***,an analytical model is developed based on analytical and proven empirical correlations governing conductive,radiative,and convective heat transfers at the steady state to estimate the ampacity of the bipolar cable system at reduced *** was shown that the proposed analytical model can be used for atmospheric pressure and systems with a larger number of poles,expanding its range of *** results of the FEM and analytical models correlate at wide ranges of parameters such as ambient temperature,duct size,distance between the positive and negative pole cables,and the overall diameter of the *** influence of horizontal and vertical arrangement of poles is included in the analytical *** results of this study can be used to design bipolar MVDC power cable systems for the envisaged wide-body AEA.

关键词： bipolar power thermal

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A review of artificial intelligence based malware detection using deep learning

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Materials Today: Proceedings 2023年 80卷 2678-2683页

作者： Mustafa Majid, Al-Ani Alshaibi, Ahmed Jamal Kostyuchenko, Evgeny Shelupanov, Alexander Department of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk634050 Russia

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art. © 2021

关键词： Malware

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Adaptive multi-channel Bayesian graph attention network for IoT transaction security

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Digital Communications and Networks 2024年第3期10卷 631-644页

作者： Zhaowei Liu Dong Yang Shenqiang Wang Hang Su School of Computer and Control Engineering Yantai UniversityYantai264005China Department of Electronics Information and BioengineeringPolitecnico di MilanoMilan20133Italy

With the rapid advancement of 5G technology,the Internet of Things(IoT)has entered a new phase of appli-cations and is rapidly becoming a significant force in promoting economic *** to the vast amounts of data created by numerous 5G IoT devices,the Ethereum platform has become a tool for the storage and sharing of IoT device data,thanks to its open and tamper-resistant ***,Ethereum account security is necessary for the Internet of Things to grow quickly and improve people's *** modeling Ethereum trans-action records as a transaction network,the account types are well identified by the Ethereum account classifi-cation system established based on Graph Neural Networks(GNNs).This work first investigates the Ethereum transaction ***,experimental metrics reveal that the Ethereum transaction network is neither optimal nor even satisfactory in terms of accurately representing transactions per *** flaw may significantly impede the classification capability of GNNs,which is mostly governed by their *** work proposes an Adaptive Multi-channel Bayesian Graph Attention Network(AMBGAT)for Ethereum account clas-sification to address this *** uses attention to enhance node features,estimate graph topology that conforms to the ground truth,and efficiently extract node features pertinent to downstream *** extensive experiment with actual Ethereum transaction data demonstrates that AMBGAT obtains competitive performance in the classification of Ethereum accounts while accurately estimating the graph topology.

关键词： Internet of things Graph representation learning Node classification Security mechanism

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Digital Selves Based Intelligent Construction Framework

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International Journal of Crowd Science 2024年第4期8卷 184-194页

作者： Liu, Xiaomeng Sun, Hongbo Lin, Jiarui School of Computer and Control Engineering Yantai University Yantai264005 China Department of Civil Engineering Tsinghua University Beijing100084 China

Construction is the pillar industry of Chinese national economy, but the profit rate has continued to decline in recent years. The conventional job-centric construction information system cannot meet the requirements for safety, quality, and efficiency. In order to solve the above problems, a digital selves based intelligent construction framework has been proposed which is centred on workers, equipment, and sites. It changes the conventional job-centered construction information system and realizes efficient coordination of information among various departments and stages. Applying the digital selves based intelligent construction framework to the case has been proved to be a good solution to the problem of information sharing. Meanwhile, the authenticity and reliability of the information have been ensured. The system has improved management efficiency and information systematization for all participants. It has enabled companies and workers to have a more comprehensive understanding of themselves and to make timely and effective strategies against hazards. In addition, the system has effectively raised the level of digitalization, automation, and intelligence of construction, making governance more accurate and more effective. © The author(s) 2024.

关键词： Efficiency

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Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

Journal of Energy Systems

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Journal of Energy systems 2025年第1期9卷 132-158页

作者： Elbaş, Hakan Bilgin, Turgay Tugay Bursa Technical University Graduate School Department of Intelligent Systems Engineering Bursa Turkey Bursa Technical University Faculty of Engineering and Natural Sciences Department of Computer Engineering Bursa Turkey

This review examines the methods, determinants, and forecasting horizons used in electricity demand forecasting in Türkiye. The study investigates how Türkiye's electricity demand is influenced by economic, climatic, socio-demographic, and technological factors, and explores the evolving landscape of forecasting techniques, from traditional statistical models to advanced machine learning and hybrid approaches. The research addresses three key questions: The significant determinants of electricity demand in Türkiye, the most effective forecasting methods, and the application of these insights to improve energy planning and policy development. Through a systematic analysis of peer-reviewed literature, official reports, and case studies, the study reveals the complex interplay of factors affecting electricity demand and the increasing sophistication of forecasting methodologies. Economic growth, industrial production, climate change, urbanization, and technological advancements emerge as primary drivers of demand, while artificial neural networks and hybrid models demonstrate superior forecasting capabilities. The study highlights the importance of integrated modeling approaches, sector-specific strategies, and the incorporation of climate projections in long-term planning. It also emphasizes the need for aligning energy policies with broader economic and environmental objectives. This review provides valuable insights for researchers, policymakers, and industry stakeholders, offering a comprehensive framework for understanding and improving electricity demand forecasting. © 2025 Erol Kurt. All rights reserved.

关键词： Demand response

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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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Scalable Self-Tallying e-Voting using Homomorphic Time-Lock Puzzles and zk-SNARKs

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IEEE Transactions on Network Science and engineering 2025年第4期12卷 2566-2581页

作者： Elsheikh, Muhammad Youssef, Amr M. Hasan, M. Anwar University of Waterloo Department of Electrical and Computer Engineering Canada Cairo Egypt Concordia University Concordia Institute for Information Systems Engineering QC Canada

Achieving fairness, verifiability, and abandon resistance poses challenges within e-voting protocols. This paper introduces a privacy-preserving self-tallying e-voting system leveraging blockchain technology. The system supports diverse e-voting models, including 'Yes/No', approval voting with multiple candidates, and score voting. By employing linearly homomorphic time-lock puzzles (LHTLPs) along with verifiable delay functions (VDFs) and zero-knowledge Succinct Non-interactive Argument of Knowledge schemes (zk-SNARKs), the proposed system ensures crucial security properties, including voter anonymity and eligibility, as well as ballot privacy and validity. It also provides efficient individual and universal verifiability (end-to-end verifiability), and dispute-freeness. More importantly, the system demonstrates fairness and abandon resistance. Furthermore, the evaluation of the proof-of-concept implementation on the Ethereum blockchain indicates that on-chain gas costs are either fixed or increasing slowly and logarithmically with the number of voters. © 2013 IEEE.

关键词： Blockchain

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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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