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A crayfish optimised wavelet filter and its application to fault diagnosis of machine components

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第3-4期135卷 1825-1837页

作者： Chauhan, Sumika Vashishtha, Govind Zimroz, Radoslaw Kumar, Rajesh Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Grobli 15 PL-50421 Wroclaw Poland St Longowal Inst Engn & Technol Dept Mech Engn Precis Metrol Lab Longowal 148106 India Graphic Era Deemed be Univ Dept Mech Engn Dehra Dun India

Industrial machinery relies heavily on accurate fault diagnosis to maintain its reliability and operational efficiency. However, analyzing vibration signals for early fault detection is complex, as these signals often suffer from low signal-to-noise ratios, background noise, and random interferences. While wavelet filters are frequently employed for identifying informative frequency bands, determining the optimal filter parameters is crucial for accurately extracting repetitive transients related to faults. This research proposes a novel approach that utilises a crayfish optimization algorithm (COA) to adaptively optimise the wavelet filter. The COA employs correlated kurtosis (CK) as a fitness function to guide the optimization process. This method addresses limitations related to inaccurate CK period estimation through a continuous update mechanism, ensuring more precise parameter selection. Through its application to various industrial cases, the proposed methodology demonstrates its superiority over existing techniques in accurately extracting informative frequencies. This advancement enables more precise fault diagnosis, leading to improved maintenance strategies and minimized downtime in industrial environments.

关键词： Correlated kurtosis crayfish optimization algorithm Morlet wavelet Denoising filter Bearing defects

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Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

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PHYSICA SCRIPTA 2024年第7期99卷

作者： Xavier, Jasmine A. Vanitha, Suthanthira N. Sudha, G. Birunda, M. Jayaraj Annapackiam CSI Coll Engn Elect & Commun Engn Nazareth 628617 Tamil Nadu India Muthayammal Engn Coll Autonomous Elect & Elect Engn Rasipuram 637408 Tamil Nadu India Muthayammal Engn Coll Autonomous Biomed Engn Rasipuram 637408 Tamil Nadu India

The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is its heavy energy consumption while transmitting data, which makes scaling difficult. To overcome such issues, a hybrid approach of crayfish optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD, node localization is performed that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering, along with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection, combining data analysis for optimizing energy utilization in heterogeneous IoT environments. The 'combined CFO-FCM and SSA-RBM' has been implemented in MATLAB and achieved an accuracy of 94.50%. As a result, the overall performance of the system is improved.

关键词： cluster head selection crayfish optimization algorithm fuzzy C means clustering heterogeneous IoT wireless sensor network

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Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics

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SCIENTIFIC REPORTS 2024年第1期14卷 1-12页

作者： Cuk, Aleksa Bezdan, Timea Jovanovic, Luka Antonijevic, Milos Stankovic, Milos Simic, Vladimir Zivkovic, Miodrag Bacanin, Nebojsa Singidunum Univ Danijelova 32 Belgrade 11010 Serbia Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Yuan Ze Univ Coll Engn Dept Ind Engn & Management Taoyuan City 320315 Taiwan Korea Univ Coll Informat 145 Anam Ro Seoul South Korea Middle East Univ MEU Res Unit Amman Jordan INTI Int Univ Fac Data Sci & Informat Technol Nilai 71800 Malaysia

Parkinson's disease (PD) is a progressively debilitating neurodegenerative disorder that primarily affects the dopaminergic system in the basal ganglia, impacting millions of individuals globally. The clinical manifestations of the disease include resting tremors, muscle rigidity, bradykinesia, and postural instability. Diagnosis relies mainly on clinical evaluation, lacking reliable diagnostic tests and being inherently imprecise and subjective. Early detection of PD is crucial for initiating treatments that, while unable to cure the chronic condition, can enhance the life quality of patients and alleviate symptoms. This study explores the potential of utilizing long-short term memory neural networks (LSTM) with attention mechanisms to detect Parkinson's disease based on dual-task walking test data. Given that the performance of networks is significantly inductance by architecture and training parameter choices, a modified version of the recently introduced crayfish optimization algorithm (COA) is proposed, specifically tailored to the requirements of this investigation. The proposed optimizer is assessed on a publicly accessible real-world clinical gait in Parkinson's disease dataset, and the results demonstrate its promise, achieving an accuracy of 87.4187 % for the best-constructed models.

关键词： Parkinson's disease Medical diagnosis Long-short term memory neural networks crayfish optimization algorithm optimization

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Artificial Neural Networks with Soft Attention: Natural Language Processing for Phishing Email Detection Optimized with Modified Metaheuristics 4th

Artificial Neural Networks with Soft Attention: Natural Lang...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Lakicevic, Bojana Spalevic, Zaklina Volas, Igor Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Univ Montenegro Fac Law Cetinjski Put 2 Podgorica 81000 Montenegro Singidunum Univ Danijelova 32 Belgrade 11010 Serbia Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade 11000 Serbia

ISBN: (纸本)9783031837890;9783031837906

This study presents a novel approach to phishing email detection, leveraging artificial neural networks (ANN) with soft attention in natural language processing (NLP) through the integration of BERT encoders. Addressing the critical need for effective aritifical intelligence (AI)-driven cybersecurity solutions, this research combines BERT's NLP capabilities with a modified crayfish optimization algorithm (COA) to fine-tune the hyperparameters of deep neural network models, enhancing classification accuracy. Experimental results show that our optimized model achieves a phishing detection accuracy of 92.5%, outperforming several high-performing optimizers. Comparative analysis demonstrates that this approach offers superior detection capabilities, underscoring its potential for real-world applications. This work advances the field by refining BERT's application with optimization algorithms and provides a valuable framework for future cybersecurity research.

关键词： ANN soft attention NLP BERT metaheuristics optimization crayfish optimization algorithm phishing detection cybersecurity

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Energy efficient data dissemination in wireless sensor network enabled IoT using domain-adaptive message passing graph neural network

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第13期37卷 e5825-e5825页

作者： Shiny, G. Susan Ram, R. Saravana Rajeshkumar, J. Muthalakshmi, M. Sethu Inst Technol Dept Informat Technol Kariapatti TK NH 45BTuticorin Madurai Rd Thoothukudi 626115 Tamil Nadu India Anna Univ Reg Campus Dept Elect & Commun Engn Madurai Tamil Nadu India RL Jalappa Inst Technol Dept Comp Sci & Engn Doddaballapur Karnataka India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & Dept Bio Med Engn Chennai Tamil Nadu India

In the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over-the-air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network-enabled IoT using Domain-Adaptive Message Passing Graph Neural Network (EEP-WSN-IoT-DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the crayfish optimization algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain-adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum-distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL-RPDT-WSN), Reinforcement-Learning base energy effectual optimized routing protocol in WSN (RL-EERP-WSN), and Energy-efficient intellectual routing method for IoT-enabled WSN (EIR-IoT-WSN), respectively. Energy Efficient Protocol for Data Dissemination in Wireless Sensor network-enabled IoT using domain-adaptive Message Passing Graph Neura

关键词： cluster head crayfish optimization algorithm deep fuzzy curriculum clustering domain-adaptive message passing graph neural network

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Mathematical Modeling and Simulation of Traffic Flow Control in Urban Environments Using Fuzzy Deep Neural Network With optimization algorithm

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IEEE ACCESS 2024年 12卷 158322-158332页

作者： Alshardan, Amal Maashi, Mashael Alanazi, Sultan M. Darem, Abdulbasit A. Mahgoub, Hany Alliheedi, Mohammed A. Alshammeri, Menwa Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Software Engn Riyadh 11543 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 91911 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha 61421 Saudi Arabia Al Baha Univ Fac Comp & Informat Dept Comp Sci Al Baha 65779 Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia

With the enlarging number of transports on the road and fast growth, traffic flow is a significant current worry that obstructs the financial system's evolution and affects the quality of life. Intelligent Transportation Systems (ITS) utilize innovative technologies in urban environments to enhance urban and interurban traffic, decrease congestion, and improve overall traffic flow control. A usual method employed for traffic flow control is usually based upon the analysis and collection of data in a physical manner that is energy-demanding and tedious. Currently, with the improvements in Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI), urban environments are observed to induce concerns of the environment properly, with the optimum control of traffic pollution, congestion, and other effects. Hence, this article designs a Traffic Flow Control utilizing a Fuzzy Deep Neural Network with a crayfish optimization algorithm (TFCFDNN-COA) technique in Urban Environments. The TFCFDNN-COA technique mainly aims to control traffic flow levels in urban environments, allowing efficient traffic management. At first, the TFCFDNN-COA approach includes data pre-processing and a dingo optimizer algorithm (DOA) model for feature selection. The fuzzy deep neural network (FDNN) technique controls traffic flow. Eventually, the crayfish optimization algorithm (COA) model is utilized to fine-tune the best hyperparameter of the FDNN model. A wide range of experimental studies has been completed, and the outcomes have been studied using numerous features. The experimental validation of the TFCFDNN-COA approach portrayed superior MSE, MAE, and MAPE values of 0.0011, 0.0206, and 0.6738, respectively, all observed on Sunday.

关键词： Urban areas Vectors Traffic control Flow production systems Roads Real-time systems Long short term memory Feature extraction Aerospace electronics Accuracy Traffic flow control fuzzy deep neural network feature selection crayfish optimization algorithm intelligent transportation systems

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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning

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SCIENTIFIC REPORTS 2025年第1期15卷 1-31页

作者： Yameen, Muhammad Zubair Lu, Zhigang El-Sousy, Fayez F. M. Younis, Waqar Zardari, Baqar Ali Junejo, Abdul Khalique Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Key Lab Power Elect Energy Conservat & Drive Contr Qinhuangdao 066004 Peoples R China Prince Sattam bin Abdulaziz Univ Coll Engn Dept Elect Engn Al Kharj 16273 Saudi Arabia Quaid e Awam Univ Nawabshah Dept Informat Technol Nawabshah Pakistan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan Peoples R China

The increasing utilization of renewable energy sources in low-inertia power systems demands advanced control strategies for grid-forming inverters (GFMs). Conventional Model Predictive Control (MPC) methods, which depend on static models and predefined boundaries, often struggle to preserve frequency stability in dynamic grid conditions. This research presents an Adaptive Model Predictive Control (AMPC) framework to enhance GFM performance in Virtual Synchronous Machine (VSM) mode, ensuring robust frequency stability under uncertainties. The primary issue addressed is the inefficiency of traditional MPC in adapting to dynamic grid conditions. To resolve this, the AMPC framework combines offline reinforcement learning for parameter tuning with online MPC using soft constraints. The offline phase employs a novel Hybrid crayfish optimization and Self-Adaptive Differential Evolution algorithm (COA-jDE) to minimize the cost function \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$U_{offline}$$\end{document}, deriving optimal control parameters (Q, R) before real-time deployment. This process, termed cost function minimization using COA-jDE in a reinforcement learning framework, enhances GFM performance by adaptively adjusting virtual inertia and damping. Simulations on a 16MW wind-powered DFIG microgrid demonstrate that AMPC outperforms traditional MPC and VSM methods during grid disturbances, symmetrical faults, islanding, and load shifts. Furthermore, AMPC is computationally efficient compared to conventional reinforcement learning techniques, as adaptation is restricted to offline tuning. The framework not only improves compliance with grid codes (e.g., GC0137, IEEE 1547) but also provides a flexible, resilient control strategy for modern low-inertia grids.

关键词： Model Predictive Control crayfish optimization algorithm Microgrid Frequency DFIG Virtual Synchronous Machine Differential Evolution algorithm

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Frequency regulation in a hybrid renewable power grid: an effective strategy utilizing load frequency control and redox flow batteries

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SCIENTIFIC REPORTS 2024年第1期14卷 1-33页

作者： Elkasem, Ahmed H. A. Kamel, Salah Khamies, Mohamed Nasrat, Loai Aswan Univ Fac Engn Dept Elect Engn Aswan 81542 Egypt Sohag Univ Fac Engn Dept Elect Engn Sohag 82524 Egypt

Renewable energy sources (RESs) have become integral components of power grids, yet their integration presents challenges such as system inertia losses and mismatches between load demand and generation capacity. These issues jeopardize grid stability. To address this, an effective approach is proposed, combining enhanced load frequency control (LFC) (i.e., fuzzy PID- (TID mu)-D-lambda) with controlled energy storage systems, specifically controlled redox flow batteries (CRFBs), to mitigate uncertainties arising from RES integration. The optimization of this strategy's parameters is achieved using the crayfish optimization algorithm (COA), known for its global optimization capabilities and balance between exploration and exploitation. Performance evaluation against conventional controllers (PID, FO-PID, FO-(PD-PI)) confirms the superiority of the proposed approach in LFC. Extensive testing under various load disturbances, high renewables penetration, and communication delays ensures its effectiveness in minimizing disruptions. Validation using a standardized IEEE 39-bus system further demonstrates its efficiency in power networks grappling with significant renewables penetration. In summary, this integrated strategy presents a robust solution for modern power systems adapting to increasing renewable energy utilization.

关键词： Fuzzy-PID + ((TID mu)-D-lambda) controller crayfish optimization algorithm Load frequency control Renewable energy resources Controlled redox flow batteries Communication delay time

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A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish optimization algorithm to Improve ESN Network

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第3期11卷 1-13页

作者： Ma, Lili Xie, Bin Liu, Fengjun Ma, Liying Tangshan Presch Teachers Coll Yutian Branch Tangshan 063000 Hebei Peoples R China North Univ Sci & Technol Qing Gong Coll Tangshan 063000 Hebei Peoples R China Tangshan Guanghuadao Primary Sch Tangshan 063000 Hebei Peoples R China

INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch. OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods. METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements;then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed;finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments. RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real -time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms. CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.

关键词： virtual reality technology application foreign language teleplatform construction effect analysis crayfish optimization algorithm backward state neural network

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