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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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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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An authenticated spectrum acquirement and transmission of encrypted images over fusion centre

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Multimedia Tools and Applications 2024年 1-24页

作者： V, Thanikaiselvan Y, PiyaDessi S, Renuga devi Shanthini, S. Suba Mahalingam, Hemalatha S, Aashiq Banu R, Dhivya V.M, Padmapriya Amirtharajan, Rengarajan School of Electronics Engineering Vellore Institute of Technology Vellore Vellore India School of Information Technology Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia System Engineering Department Military Technological College Muscat Oman Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Tamilnadu Sivakasi626005 India Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & amp Electronics Engineering SASTRA Deemed University 613 401 Thanjavur India

These days, spectrum insufficiency is the most common issue. If the number of smartphone users who access the radio spectrum grows, the radio spectrum becomes increasingly scarce. Certain types of technology equipment are designed to work only within a specific spectrum range, and there is rivalry in specifications for various other applications. As a result, this paper effectively exploits a licensed user’s unused spectrum to reduce the crowded spectrum and eradicate the Radio spectrum shortage. Licensed primary users (PUs) and secondary users (SUs) would be accepted into a Fusion Centre (FC) based on a spectrum framework with authentication. The approved PUs begin by updating their information to FC1;thenceforth, encryption is performed on the information uploaded to FC1. This information from FC1 is stored in Cloud Storage after encryption using the Blowfish algorithm & De-oxy DNA, a fast encryption algorithm. Following that, FC2, connected to the same cloud environment, downloads the PU data uploaded. The spectrum is then allocated to the authenticated SU linked to FC2 based on signal conditions and priority. This proposed method also avoids any delay in the network and encryption by acquiring a fast encryption algorithm, making the system faster and avoiding the Primary User Emulation (PUE) attack. Later, the time taken for the Tiny Encryption Algorithm (TEA) and the Blowfish/DNA algorithm are compared to validate the run time for different input images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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Privacy, Security, Trust and Applications in Internet of Things 6

Privacy, Security, Trust and Applications in Internet of Thi...

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6th IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Yaklaf, Sliman K.A.A. Elmezughi, Abdurrezagh S. Naas, Salah Mohamed H. Ekreem, Nasser Bashir Azzaytuna University Faculty of Engineering Electrical and Electronics Engineering Department Tarhuna Libya Azzaytuna University Faculty of Engineering Computer Engineering Department Tarhuna Libya

ISBN: (纸本)9798350321029

Internet of Things (IoT) is a network to connect anything with the Internet. While its purpose is to provide efficient and effective solutions, security of the network devices is a challenging issue. Therefore, security and privacy has emerged as a significant challenge for the IoT. Within the available Internet infrastructure, IoT devices collecting data through wireless technology. Now Days, IoT is research area with many devices are connected through internet. IoT concept brings a new challenge concerning privacy, trust, security, and reliability. To build smart systems, IoT technology has become important technology for people use. In this paper, IoT technologies, applications and characteristics will be briefly discussed. This paper gives a complete image of IoT security issues and its applications. The platform is required to secure exchange between consumers of their information and IoT devices. © 2023 IEEE.

关键词： Internet of things

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Blockchain-based color medical image cryptosystem for industrial Internet of Healthcare Things (IoHT)

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Multimedia Tools and Applications 2024年 1-55页

作者： Khallaf, Fatma El-Shafai, Walid El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Ahram Canadian University 6th October City Giza Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

In recent years, the proliferation of smart devices and associated technologies, such as the Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Medical Things (IoMT), has witnessed a substantial growth. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptography techniques inadequate. To address these challenges, blockchain (BC) technology has emerged as a promising solution. This study introduces an efficient framework for the Internet of Healthcare Things (IoHT), presenting a novel cryptosystem for color medical images using BC technology in conjunction with the IoT, Secure Hash Algorithm 256-bit (SHA256), shuffling, and bitwise XOR operations. The encryption scheme is specifically designed for an IIoT grid network computing system, relying on diffusion and confusion principles. In this paper, the proposed cryptosystem strength is evaluated against differential attacks with several comprehensive metrics. Simulation results and theoretical analysis demonstrate the cryptosystem effectiveness, showcasing its ability to provide high levels of security and immunity to data leakage. The proposed cryptosystem offers a versatile range of technical solutions and strategies that are adaptable to various scenarios. The evaluation metrics, with approximate values of 99.61% for Number of Pixels Change Rate (NPCR), 33.46% for Unified Average Changed Intensity (UACI), and 8 for information entropy, closely align with the desired ideal outcomes. Consequently, this paper contributes to the advancement of secure and private systems for medical image encryption based on BC technology, potentially mitigating the risks associated with cyberattacks on smart medical devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Joint V2V Group Association and Transmission Power control in Federated Vehicular Networks

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IEEE Internet of Things Journal 2025年第11期12卷 18167-18182页

作者： Liu, Su Guan, Peiyuan Li, Yushuai Li, Tianyi Zolfagharian, Zolaikha Yu, Jiong Huang, Tingwen Xinjiang University School of Computer Science and Technology Urumqi830046 China University of Oslo Department of Informatics Oslo0316 Norway Aalborg University Department of Computer Science Aalborg9220 Denmark University of Tehran School of Electrical and Computer Engineering Iran Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

With growing awareness of privacy protection, Federated Learning (FL) in vehicular network scenarios effectively addresses privacy concerns, leading to the development of Federated Vehicular Networks (FVN). In FVN, vehicles maintain a global model by transmitting local models and iterative processing, resulting in significant communication overhead. In the Internet of Vehicles (IoV), as the majority of the spectrum resources are allocated to Vehicle-to-Vehicle (V2V) communication, vehicles engaged in FL encounter uplink interference when these resources are reused. This compromises the efficacy of FL vehicle communication. To address this issue, we propose FedCDC, a new Federated learning framework with adaptive weight Clustering with knowledge Distillation and Channel sharing-based resource allocation. First, we implement a communication compression strategy based on clustering and distillation to alleviate transmission load. Then, we develop a resource allocation strategy to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) for both FL vehicles and V2V groups, which investigates two critical components: channel pairing and power coordination. We formulate the optimization issue as a multi-objective optimization problem that is solved offline using the MIDACO solver. Due to the dynamic characteristics of FVN, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enhance efficiency. Finally, extensive experiments demonstrate that our approach significantly reduces data transmission and offers an efficient resource coordination plan, thus improving FL communication efficiency and ensuring the Quality of Service (QoS) for both V2V groups and FL vehicles. © 2014 IEEE.

关键词： Differential privacy

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Forecasting of Solar Power Potential in Pakistan Using Deep Learning 3

Forecasting of Solar Power Potential in Pakistan Using Deep ...

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3rd International Conference on Emerging Trends in electrical, control, and Telecommunication engineering, ETECTE 2024

作者： Din, Zagham Muhio Uzair, Muhammad Abbas, Ghulam Saad, Arqam Khan, Shahraiz Farooq, Umar Institute of Electrical Electronics and Computer Engineering University of the Punjab Lahore Pakistan Department of Electrical Engineering Faculty of Engineering Islamic University of Madinah Madinah Saudi Arabia Department of Electrical Engineering The University of Lahore Lahore Pakistan Department of Electronics Government College University Lahore Pakistan

ISBN: (纸本)9798331504861

In power systems, renewable energy type, solar energy is significant in its present utilization and penetration;thus, there is a need for deterministic models for its forecasting. This work aims to develop an effective deep-learning model for predicting solar power potential in Pakistan, especially in the following cities: Lahore, Karachi, Peshawar, Quetta, and Islamabad. Three deep learning models, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) were used to forecast the solar irradiance by using performance measures of root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) was employed. According to the results from different models, it is revealed that CNN outperformed all the models in most of the cities. In the case of Lahore city, CNN achieved RMSE 0.638, but ANN and LSTM achieved 0.0648 and 0.0638, respectively. The results indicated that CNN had a high level of performance and stability in all the studied cities;thus, it was the most suitable model for forecasting solar power in Pakistan. ©2024 IEEE.

关键词： Solar irradiance

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A survey of artificial intelligence models for wireless capsule endoscopy videos for superior automatic diagnosis: problems and solutions

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Multimedia Tools and Applications 2025年 1-35页

作者： El-Gammal, Eman M. El-Shafai, Walid Taha, Taha E. El-Fishawy, Adel S. Abd El-Samie, Fathi E. Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Center of Advanced Software and Biomedical Clinical Engineering Consultations Faculty of Engineering Cairo University Giza Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University 84428 Riyadh11671 Saudi Arabia

Wireless Capsule Endoscopy (WCE) emerged as an innovative and patient-centric approach for non-invasive and painless examination of the gastrointestinal (GI) tract. It serves as a pivotal tool in helping medical practitioners in the early detection of anomalies within the intricate domain of the human GI tract. The automated identification of aberrations assumes paramount significance, contributing not only to temporal efficiency but also to timely diagnosis. Within the realm of academic literature, a plethora of Artificial Intelligence (AI) methodologies have been proposed to effectuate the automatic classification, segmentation, and synthesis of anomalies inherent in WCE images. This scholarly endeavor undertakes a comprehensive and meticulous survey, elucidating the spectrum of anomaly classification, summarization, and detection techniques harnessed for computer-Aided Diagnosis (CAD) in the context of WCE images. The survey begins by delineating the methodologies underpinning WCE image classification and video processing. Subsequently, an exhaustive evaluation of techniques for analyzing WCE images is furnished, accompanied by an in-depth exploration of pertinent surveys, in addition to a judicious assessment of their merits and demerits. Furthermore, this study undertakes a rigorous evaluation of prevailing datasets utilized to benchmark the efficacy of various WCE techniques. In summary, this survey serves as a vanguard, delineating promising avenues for future investigations in the realm of WCE image analysis models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Endoscopy

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Development and Investigation of Vision System for a Small-Sized Mobile Humanoid Robot in a Smart Environment

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International Journal of Crowd Science 2025年第1期9卷 29-43页

作者： Abu-Jassar, Amer Tahseen Attar, Hani Amer, Ayman Lyashenko, Vyacheslav Yevsieiev, Vladyslav Solyman, Ahmed Department of Computer Science College of Information Technology Amman Arab University Amman11937 Jordan Faculty of Engineering Zarqa University Zarqa2000 Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Department of Media Systems and Technology Kharkiv National University of Radio Electronics Kharkiv61166 Ukraine Department of Electrical and Electronics Engineering Faculty of Engineering and Architecture Nişantaşı University Istanbul34398 Turkey

The conducted research aims to develop a computer vision system for a small-sized mobile humanoid robot. The decentralization of the servomotor control and the computer vision systems is investigated based on the hardware solution point of view, moreover, the required software level to achieve an efficient matched design is obtained. A computer vision system using the upgraded tiny-You Only Look Once (YOLO) network model is developed to allow recognizing and identifying objects and making decisions on interacting with them, which is recommended for crowd environment. During the research, a concept of a computer vision system was developed, which describes the interaction between the main elements, on the basis of which hardware modules were selected to implement the task. A structure of information interaction between hardware modules is proposed, and a connection scheme is developed, on the basis of which a model of a computer vision system is assembled for research, with the required algorithmic and software for solving the problem. To ensure the high speed of the computer vision system based on the ESP32-CAM module, the neural network was improved by replacing the Visual Geometry Group 16 (VGG-16) network as the base network for extracting the functions of the Single Shot Detector (SSD) network model with the tiny-YOLO lightweight network model, which made it possible to preserve the multidimensional structure of the network model feature graph, resulting in increasing the detection accuracy, while significantly reducing the amount of calculations generated by the network operation, thereby significantly increasing the detection speed, due to a limited set of objects. Finally, a number of experiments were carried out, both in static and dynamic environments, which showed a high accuracy of identifications. © The author(s) 2025.

关键词： Anthropomorphic robots

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Predefined performance-based model-free adaptive fractional-order fast terminal sliding-mode control of MIMO nonlinear systems

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ISA Transactions 2022年 131卷 108-123页

作者： Esmaeili, Babak Salim, Mina Baradarannia, Mahdi Department of Control Engineering Faculty of Electrical and Computer Engineering University of Tabriz 29 Bahman Blvd. Tabriz *** Iran

The purpose of this article is to tackle with the problem of data-driven robust control of multi-input multi-output discrete-time nonlinear plants under tracking error constraints and output perturbations. Thereby, based upon the concept of dynamic linearization, a novel predefined performance based model-free adaptive fractional-order fast terminal sliding-mode controller is proposed so that the tracking errors can converge and remain within a preassigned neighborhood. The presented approach does solely rely on the real-time input/output data of the process, and the transient response together with the steady-state manner of the errors can be arbitrarily predefined. In the meantime, the closed-loop behavior is investigated by mathematical analysis, and the efficiency of the method is validated through various simulation examples. © 2022 ISA

关键词： MIMO systems

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