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A Decentralized and TCAM-Aware Failure Recovery Model in Software Defined Data Center Networks

computers, Materials & Continua 2025年第1期82卷 1087-1107页

作者： Suheib Alhiyari Siti Hafizah AB Hamid Nur Nasuha Daud Department of Computer System and Technology University of MalayaKuala Lumpur50603Malaysia

Link failure is a critical issue in large networks and must be effectively *** software-defined networks(SDN),link failure recovery schemes can be categorized into proactive and reactive *** schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow *** SDN adoption grows,ensuring efficient recovery from link failures in the data plane becomes *** particular,data center networks(DCNs)demand rapid recovery times and efficient resource utilization to meet carrier-grade *** paper proposes an efficient Decentralized Failure Recovery(DFR)model for SDNs,meeting recovery time requirements and optimizing switch memory resource *** DFR model enables switches to autonomously reroute traffic upon link failures without involving the controller,achieving fast recovery times while minimizing memory *** employs the Fast Failover Group in the OpenFlow standard for local recovery without requiring controller communication and utilizes the k-shortest path algorithm to proactively install backup paths,allowing immediate local recovery without controller intervention and enhancing overall network stability and *** employs flow entry aggregation techniques to reduce switch memory *** of matching flow entries to the destination host’s MAC address,DFR matches packets to the destination switch’s MAC *** reduces the switches’Ternary Content-Addressable Memory(TCAM)***,DFR modifies Address Resolution Protocol(ARP)replies to provide source hosts with the destination switch’s MAC address,facilitating flow entry aggregation without affecting normal network *** performance of DFR is evaluated through the network emulator Mininet 2.3.1 and Ryu 3.1 as SDN *** different number of active flows,number of hosts per edge switch,and different network sizes,the proposed model outperformed vario

关键词： Software defined networking failure detection failure recovery restoration protection TCAM size

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Optimization of Magnetization State Manipulation in Variable-Flux PMSMs

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IEEE Transactions on Industry Applications 2025年第3期61卷 3862-3876页

作者： Orviz, Marcos Tiemblo, Javier G. Laborda, Diego F. Reigosa, David Guerrero, Juan M. Department of Electrical Electronic Computer and System Engineering University of Oviedo Gijon Spain

Variable-flux permanent magnet synchronous machines (VF-PMSMs) were proposed to avoid the additional losses of conventional PMSMs during flux weakening (FW) operation at high speeds, as they allow dynamic manipulation of the magnetization state (MS) of the permanent magnets (PMs). However, the effect of eddy currents induced in the PMs during MS manipulation has not yet been analyzed;this issue is evaluated in this paper. Different current waveforms (i.e., pulse, trapezoidal, and half-sinusoidal signals) will be compared regarding MS manipulation capabilities and energy consumption considering PMs eddy current effects, to identify the most advantageous one. © 1972-2012 IEEE.

关键词： Permanent magnets

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An artificial intelligence-based approach for avoiding traffic congestion in connected autonomous vehicles

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International Journal of Vehicle Autonomous systems 2025年第1期18卷 81-103页

作者： Bektache, Djamel Ghoualmi-Zine, Nassira Network and System Laboratory Computer sciences Department Mohamed Cherif Messaadia University Souk Ahras Algeria Network and System Laboratory Computer sciences Department Badji-Mokhtar Annaba University Annaba Algeria

The Internet of Vehicles (IoV) has led to the emergence of sustainable smart roads. Recent advancements in this field have focused on improving traffic flow and reducing congestion using intelligent systems. In this paper, we propose a novel approach called the ‘Traffic Congestion Avoidance Approach (TCAA)’. Our approach leverages IoV technologies and deep learning algorithms to create a more responsive and efficient traffic management system. The IoV model facilitates communication between autonomous vehicles, allowing them to coordinate movements and optimise traffic flow seamlessly. Additionally, deep learning algorithms analyse real-time data, to predict and mitigate congestion dynamically. The performance evaluation of TCAA demonstrates the potential of intelligent traffic regulation systems. The union of IoV and deep learning technologies provides a robust solution to contemporary traffic challenges, paving the way for smarter, more sustainable urban mobility. This research underscores the transformative potential of AI-powered IoV systems in creating the smart roads, ultimately enhancing the quality of life in smart cities. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Traffic congestion

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Investigating the Impact of Hyper Parameters on Intrusion Detection system Using Deep Learning Based Data Augmentation

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 523-532页

作者： Iftikhar, Umar Ali, Syed Abbas Department of Computer & Information System Engineering NED University of Engineering & Technology Karachi Pakistan

The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this study. The purpose of this research is to find out how they affect the data augmentation to enhance stability during training. In this research, the degree of system performance is measured using the Classification Model Utility approach. For this reason, this study aims to determine the interaction between learning rate, augmentation percentage and epoch value when using WGAN-GP to generate synthetic data for the recognition of the system performance. The results will provide the indications on how some of the hyper parameters can be adjusted up or down for having positive or negative consequences on the generation process for further research and use of WGAN-GP. It also provides insights into how the generative model is trained, and how that affects stability and quality of the result in various settings such as image synthesis or other generative tasks. © (2025), (Science and Information Organization). All rights reserved.

关键词： Deep learning

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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

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Advances in Atmospheric Sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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Hybrid Approach of LDA, ROS-NCL, and CNN for Topic Mining in Fintech User Reviews

Hybrid Approach of LDA, ROS-NCL, and CNN for Topic Mining in...

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2025 International Conference on Advancement in Data Science, E-learning and Information system, ICADEIS 2025

作者： Hartono, Pieter Chang Widiantoro, Albertus Dwiyoga Harnadi, Bernadinus Nugroho, Erdhi Widyarto Faculty of Computer Science Semarang Information System Department Indonesia

ISBN: (纸本)9798331513320

The development of digital payment technology, such as Gopay, has provided convenience for the Indonesian public. To understand user satisfaction and identify key issues, this study analyzes Gopay user reviews using a hybrid approach. The main issues addressed are the imbalance in review data and the lack of studies integrating Latent Dirichlet Allocation (LDA) and Convolutional Neural Network (CNN) models for topic analysis. This research encompasses three main stages: topic identification using LDA, data balancing with Random Over Sampling and Neighborhood Cleaning Rule (ROS-NCL), and topic classification using CNN. The model is designed to accurately identify and classify key topics. Furthermore, this method is compared with other approaches to assess its advantages. The research findings show that this hybrid approach is effective in addressing data imbalance, achieving a topic classification accuracy of 96%. These findings provide valuable insights for service providers to improve the quality of Gopay services based on topic analysis derived from user feedback. © 2025 IEEE.

关键词： Convolutional neural networks

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Implementation of Safety-Related Intrusion Detection for an Industrial Robotic Arm

Implementation of Safety-Related Intrusion Detection for an ...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Haydar, Georgio Abou Telawi, Samer Borcsok, Josef Architecture and System Programming University of Kassel Department of Computer Kassel Germany

ISBN: (纸本)9798331529604

Ensuring the safety of human workers collaborating with industrial robots is paramount. This research work presents a novel approach by developing a safety-related intruder detection system for the operational zones of industrial robots, following the IEC 61508 standard and adopting the related redundant architecture. The hardware integrates light curtains and radar sensors in a dual-layer configuration, while the software utilizes a safety system-on-chip to implement real-time diagnostics and fault tolerance. Protecting employees and ensuring a smooth and safe work environment are significant concerns for all companies. As a result, numerous standards and regulations have been established to provide safe collaboration between humans and machines. However, despite these measures, accidents continue to occur, resulting in varying degrees of injuries. The proposed system is depicted through a comprehensive schematic, emphasizing the integration of safety measures within the robotic environment. The detailed prototype demonstrates practical application, show-casing the system's ability to detect intrusions, initiate emergency responses, and transition to fail-safe states during component failures. This research work advances safety protocols in human-robot collaboration within industrial settings, fostering a safe working environment through a well-defined safety framework. © 2025 IEEE.

关键词： Industrial robots

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Generating Multi-objective Fronts from Streamed Data Using Nested List 27th

Generating Multi-objective Fronts from Streamed Data Using ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mukherjee, Arnabi Mandal, Sourab Dutta, Paramartha Department of Computer and System Sciences Visva-Bharati WB Santiniketan India

ISBN: (纸本)9783031781889

In the sphere of managing multiple, conflicting objectives concurrently, non-dominated sorting emerges as a pivotal method guiding decision-making towards optimal solutions by generating one or more Fronts. While numerous algorithms exist for multi-objective non-dominated sorting on static data points, there remains a scarcity of the same on streamed or online data points. This study focuses on the critical realm of handling real-time or online data streams to craft an algorithm specifically tailored to manage such real-time and critical data scenarios. Furthermore, this research not only introduces a novel algorithm that utilizes a simple yet effective nested list structure mechanism to perform the task of non-dominance sorting for streamed data but also evaluates its performance by checking its correctness with the existing Fast Non-dominated Sorting algorithm which is used in both Non-dominated Sorting Genetic Algorithm (NSGA-III) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The efficacy of the algorithm is also proven by showing its applicability on numerous benchmark datasets. The proposed mechanism shows complexity O(MN) in terms of space, whereas O(MN) is the time complexity in the best-case scenario, and the worst-case as well as average-case complexity for the same is O(MN2). Here, M denotes the number of objective functions and N indicates the population size. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Data streams

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in Engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer

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Neural Computing and Applications 2025年第6期37卷 4155-4183页

作者： Braik, Malik Sheta, Alaa Aljahdali, Sultan El-Hefnawi, Fatma Al-Hiary, Heba Elashmawi, Walaa H. Computer Science Department Al-Balqa Applied University Salt Jordan Department of Computer Science Southern Connecticut State University New HavenCT United States Computer Science Department Taif University Taif Saudi Arabia Microwave Department Electronics Research Institute Giza Egypt Department of Computer Information System Al-Balqa Applied University Salt Jordan Department of Computer Science Faculty of Computers & Informatics Suez Canal University Ismailia Egypt Department of Computer Science Faculty of Computer Science Misr International University Cairo Egypt

Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Simulated annealing

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