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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Using Simulation and DT to Improve Performance of systems Operation Modeling 23

Using Simulation and DT to Improve Performance of Systems Op...

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Proceedings of the 33rd Annual International Conference on computer science and Software Engineering

作者： Grabarnik Genady Ya. Poltronieri Filippo Tortonesi Mauro Zaccarini Mattia Shwartz Larisa Department of Mathematics and Computer Science St. John’s University Queens New York USA Distributed Systems Research Group University of Ferrara Ferrara Italy AI for IT IBM T.J. Watson Research Center Yorktown Heights New York USA

The ever-increasing complexity of applications and networks man-agement demands alternative solutions for identifying of suitable deployment configurations in line with established Service Level Agreements. Some existing approaches utilize methods such as mathematical simulation and application re-enactment under a sim-ulated load implemented as digital twin. In this paper, we describe a mixed-modal framework for varying composition of the above approaches hence improving the performance of application’s ser-vice level modeling further. This framework facilitates the best simulation model performance at any given time by anticipating the model drift and by continuously monitoring performance of the mixed-modal simulation model.

关键词： System Operation Modeling Simulation Mathematical Simulation Re-Enactment Simulation Digital Twin

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Exploring Y1 Communications and Services in O-RAN: Background, Privacy, and Security

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4638-4666页

作者： Sachinkumar B. Mallikarjun Mohammad Asif Habibi Mihirraj Dixit Xavier Costa-Pérez Mérouane Debbah Hans D. Schotten Department of Electrical and Computer Engineering Division of Wireless Communications and Radio Navigation University of Kaiserslautern (RPTU) Kaiserslautern Germany 6G Networks Research and Development Department NEC Laboratories Europe Heidelberg Germany Network Engineering Department Universitat Politècnica de Catalunya Barcelona Spain Department of AI Driven Systems i2CAT Research Center Barcelona Spain Department of AI Driven Systems Catalan Institution for Research and Advanced Studies (ICREA) Barcelona Spain Department of Computer and Information Engineering KU 6G Research Center Khalifa University of Science and Technology Abu Dhabi UAE CentraleSupelec University Paris-Saclay Gif-sur-Yvette France Intelligent Networking Research Group German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

In the latest open radio access network (O-RAN) architecture, the Y1 interface has been introduced to grant authorized parties, known as Y1 consumers, access to and utilization of Y1 data within the near-real-time RAN intelligent controller (Near-RT RIC). The primary objective of enabling communication between Y1 consumers and the Near-RT RIC is to facilitate the introduction of value-added services to O-RAN. This paper comprehensively explores Y1 communications, services, interface, data, and other essential concepts required to understand the subject matter thoroughly. We examine the security and privacy aspects of Y1 communications to ensure that all authorized parties involved in these interactions are equipped to withstand potential threats and vulnerabilities. More importantly, our research proposes a novel architectural framework for Y1 communications that integrates advanced functionalities and specifies secure communication protocols. This framework addresses the limitations of the state-of-the-art Y1 architecture by enhancing its robustness, adaptability, and efficiency. In addition, we outline the procedural flow for Y1 communications within the proposed framework, detailing the functionalities and processes it introduces. Beyond security and privacy procedures, this paper elaborates on the various phases of Y1 communications. Furthermore, a series of experiments are conducted to evaluate the effectiveness of the proposed Y1 framework. Our assessment focuses on the authentication and authorization schemes, revealing that the mutual transport layer security (mTLS) 1.3-based certificate-bound access token (CBAT) scheme demonstrated superior performance in ensuring secure and efficient communication. Additionally, the robustness of the proposed architecture is validated by evaluating its functionality in both subscription and query scenarios. Moreover, the paper discusses the proposed Y1 framework, primarily focusing on its role and functionality within the

关键词： Open RAN Security Privacy Protocols computer architecture Authentication Standards Robustness Guidelines Authorization

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Peer Discovery in Tree-Structured P2P Overlay networks by Means of Connected Dominating Sets

Peer Discovery in Tree-Structured P2P Overlay Networks by Me...

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Conference on Local computer networks (LCN)

作者： Peter Detzner Jana Gö deke Steffen Bondorf Department for AI and Autonomous Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Faculty of Computer Science Distributed and Networked Systems Group Ruhr University Bochum Bochum Germany

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480024

A Peer-to-Peer (P2P) network consists of a large number of nodes, where each node may have different capabilities and properties. Finding peers with specific capabilities and properties is challenging. Thus, we propose a practical solution to the problem of peer discovery, which is finding peers in the network according to a specified query. We contribute a peer discovery for an m-ary tree-structured P2P network by utilizing a connected dominating set (CDS), a technique that is typically used in unstructured networks. Our approach of constructing the CDS requires no additional communication cost, while nodes can insert, update and remove data within ${\mathcal{O}}(1)$. Each node of the CDS – a dominating set node – maintains only a limited number of nodes. We confirm the properties of our proposed solution by using the ns-3 discrete-event simulator. This includes, besides the degree of decentralism of the peer discovery, also the heterogeneity of peers.

关键词： Overlay networks Upper bound Runtime Costs Peer-to-peer computing

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Modeling Digital Twins of Kubernetes-Based Applications

Modeling Digital Twins of Kubernetes-Based Applications

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IEEE Symposium on computers and Communications (ISCC)

作者： Davide Borsatti Walter Cerroni Luca Foschini Genady Ya. Grabarnik Filippo Poltronieri Domenico Scotece Larisa Shwartz Cesare Stefanelli Mauro Tortonesi Mattia Zaccarini University of Bologna Bologna Italy Department of Mathematics and Computer Science St. John's University Queens NY USA Distributed Systems Research Group University of Ferrara Ferrara Italy IBM TJ W2atson Research Center Operational Innovation NY USA

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural networks (CNN) and Radial Basis Function networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Classification and evaluation of IoT brokers: A methodology

Classification and evaluation of IoT brokers: A methodology

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作者： Bertrand-Martinez, Eddas Dias Feio, Phelipe Brito Nascimento, Vagner de Kon, Fabio Abelém, Antônio GERCOM - Research Group on Networks and Multimedia Communications Federal University of Pará Belém Brazil Department of Computer Science University of São Paulo São Paulo Brazil

Since the term Internet of Things (IoT) was coined by Kevin Ashton in 1999, a number of middleware platforms have been developed to cope with important challenges such as the integration of different technologies. In this context of heterogeneous technologies, IoT message brokers become critical elements for the proper function of smart systems and wireless sensor networks (WSN) infrastructures. There are several evaluations made on IoT messaging middleware performance. Nevertheless, most of them ignore crucial aspects of the IoT context that also need to be included, such as reliability and other qualitative aspects. Thus, in this article, we propose a methodology for classification and evaluation of IoT brokers to help the scientific community and technology industry on evaluating them according to their interests, without leaving out important aspects for the context of smart environments. Our methodology bases its qualitative evaluations on the ISO/IEC 25000 (SQuaRE) set of standards and its quantitative evaluations on Jain's process for performance evaluation. We developed a case study to illustrate our proposal with 12 different open-source brokers, validating the feasibility of our methodological approach. © 2020 John Wiley & Sons, Ltd.

关键词： Middleware

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Exploiting Federated Learning for EEG-based Brain-computer Interface System

Exploiting Federated Learning for EEG-based Brain-Computer I...

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Omni-layer Intelligent systems (COINS), International Conference on

作者： Mohammadnavid Ghader Bahar Farahani Zahra Rezvani Mahyar Shahsavari Mahmood Fazlali Department of Computer Science Shahid Beheshti University Tehran Iran Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computer Science Alzahra University Tehran Iran Donders Institute Radboud University Nijmegen The Netherlands Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients.

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Prediction of the final size for COVID-19 epidemic using machine learning:A case study of Egypt

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Infectious Disease Modelling 2020年第1期5卷 622-634页

作者： Lamiaa A.Amar Ashraf A.Taha Marwa Y.Mohamed Networks and Distributed Systems Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Multimedia and Computer Graphics Department Informatics Research InstituteCity of Scientific Research and Technological ApplicationsSRTA-CITYEgypt Department of Mathematics and Computer Science Faculty of ScienceAlexandria UniversityEgypt

COVID-19 is spreading within the sort of an enormous epidemic for the *** epidemic infects a lot of individuals in *** World Health Organization states that COVID-19 could be spread from one person to another at a very fast speed through contact and respiratory *** these days,Egypt and all countries worldwide should rise to an effective step to investigate this disease and eliminate the effects of this *** this paper displayed,the real database of COVID-19 for Egypt has been analysed from February 15,2020,to June 15,2020,and predicted with the number of patients that will be infected with COVID-19,and estimated the epidemic final *** regression analysis models have been applied for data analysis of COVID-19 of *** this study,we’ve been applied seven regression analysis-based models that are exponential polynomial,quadratic,thirddegree,fourth-degree,fifth-degree,sixth-degree,and logit growth respectively for the COVID-19 ***,the exponential,fourth-degree,fifth-degree,and sixth-degree polynomial regression models are excellent models specially fourth-degree model that will help the government preparing their procedures for one *** addition,we have applied the well-known logit growth regression model and we obtained the following epidemiological insights:Firstly,the epidemic peak could possibly reach at 22-June 2020 and final time of epidemic at 8-September ***,the final total size for cases 1.6676Et05 *** action from government of interevent over a relatively long interval is necessary to minimize the final epidemic size.

关键词： COVID-19 Regression analysis model Epidemic model

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