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WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

Intelligent Automation & Soft Computing 2023年第2期35卷 1737-1751页

作者： C.Edwin Singh S.Maria Celestin Vigila Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationNagercoil629180India Department of Information Technology Associate ProfessorNoorul Islam Centre for Higher EducationNagercoil629180India

Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world *** are more vulnerable to security *** in nodes,band-width limits,and centralized control and management are some of the ***(Intrusion Detection System)are the aid for detection,deter-mination,and identiﬁcation of illegal system activity such as use,copying,mod-iﬁcation,and destruction of *** address the identiﬁed issues,academics have begun to concentrate on building IDS-based machine learning *** learning is a type of machine learning that can produce exceptional *** study proposes that WOA-DNN be used to detect and classify incursions in MANET(whale Optimized Deep Neural Network Model)WOA(whale Opti-mization algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and efﬁ*** a result,secure data transport to other nodes is provided,preventing intruder *** invaders are found using the(Machine Learning)ML-IDS and WOA-DNN *** data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classiﬁer is used in forward propagation to predict whether a result is normal or *** compare the traditional and proposed models’effectiveness,the accuracy of classiﬁcation,detection of the attack rate,precision rate,and F-Measure,Recall are *** proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy ***-DNN also has a greater assault detection rate than others,resulting in fewer false *** classiﬁcation accuracy of the proposed WOA-DNN model is 99.1%.

关键词： Intrusion detection system whale optimization algorithm deep neural network mobile ad-hoc networks forward and back propagation

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An Image Inpainting Method Based on whale-Integrated Monarch Butterfly optimization-Based DCNN

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2022年第3期13卷 1-23页

作者： Hudagi, Manjunath R. Soma, Shridevi Biradar, Rajkumar L. Tatyasaheb Kore Inst Engn & Technol Warananagar India Poojya Doddappa Appa Coll Engn Dept Comp Sci & Engn Kalaburagi India G Narayanamma Inst Technol & Sci Women Shaikpet India

This paper proposes an image inpainting method based on whale-integrated monarch butterfly optimization-based deep convolutional neural network (whale-MBO-DCNN) model. Initially, the patch extraction and mapping are applied to the input image to extract the patches of the image followed by image reconstruction in order to map the patches. The patch with minimum distance is selected using the concept of Bhattacharya distance in patch extraction. On the other hand, the construction of the residual image is done using deep CNN, which is trained with the proposed whaleMBO algorithm. The proposed whale-MBO algorithm is developed from the integration of monarch butterfly optimization (MBO) and WOA. Finally, the residual image and the reconstructed image are fused using holoentropy to obtain the reconstructed image. The experimentation is performed using the evaluation metrics, such as PSNR, SDME, and SSIM. The effectiveness of the proposed image inpainting method is revealed through a higher PSNR, SDME, and SSIM of 33.0585, 74.4249, and 0.9479, respectively.

关键词： Bhattacharya Distance Deep Convolutional Neural Network Image Inpainting Patch Extraction whale optimization algorithm

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Optimal evaluation of re-opening policies for COVID-19 through the use of metaheuristic schemes

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APPLIED MATHEMATICAL MODELLING 2023年第1期121卷 506-523页

作者： Cuevas, Erik Rodriguez, Alma Perez, Marco Murillo-Olmos, Jesus Morales-Castaneda, Bernardo Alejo-Reyes, Avelina Sarkar, Ram Univ Guadalajara Elect Dept CUCEI Ave Revoluc 1500 Guadalajara 44430 Jal Mexico Ind Tech Educ Ctr Software Dev Calle Nueva Escocia 1885Providencia 5a Secc Guadalajara 44638 Jal Mexico Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Panamer Univ Fac Engn Prolongac Calzada Circunvalac Poniente 49 Zapopan 45010 Jalisco Mexico

A new contagious disease or unidentified COVID-19 variants could provoke a new col-lapse in the global economy. Under such conditions, companies, factories, and organiza-tions must adopt reopening policies that allow their operations to reduce economic effects. Effective reopening policies should be designed using mathematical models that emulate infection chains through individual interactions. In contrast to other modeling approaches, agent-based schemes represent a computational paradigm used to characterize the person-to-person interactions of individuals inside a system, providing accurate simulation results. To evaluate the optimal conditions for a reopening policy, authorities and decision-makers need to conduct an extensive number of simulations manually, with a high possibility of losing information and important details. For this reason, the integration of optimization and simulation of reopening policies could automatically find the realistic scenario under which the lowest risk of infection was attained. In this paper, the metaheuristic technique of the whale optimization algorithm is used to find the solution with the minimal trans-mission risk produced by an agent-based model that emulates a hypothetical re-opening context. Our scheme finds the optimal results of different generical activation scenarios. The experimental results indicate that our approach delivers practical knowledge and es-sential estimations for identifying optimal re-opening strategies with the lowest transmis-sion risk.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Agent-based model SARS-CoV-2 COVID-19 Transmission model Metaheuristic approaches whale optimization algorithm

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Fault diagnosis of WOA-SVM high voltage circuit breaker based on PCA Principal Component Analysis

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ENERGY REPORTS 2023年 9卷 628-634页

作者： Yang, Liyuan Zhang, Kuan Chen, Zehua Liang, Yongchun Hebei Univ Sci & Technol Coll Elect & Engn 26 Yuxiang St Shijiazhuang 050018 Peoples R China

High voltage circuit breakers (HVCB) are significant protection and control devices for electric systems, and its operating state directly influences the stability and reliability of electric systems. In order to solve the problem of low precision of the mechanical diagnostic accuracy of HVCB, this paper propose a means of optimization based on the principal component analysis (PCA), and the diagnosis of the HVCB is supplemented by the support vector machine (SVM) of the whale algorithm (WOA) optimization. Firstly, the present machine fault state of the circuit breaker is experimentally simulated, the LabVIEW software is used to obtain the vibration signal of different states, then the Hilbert Huang conversion is made to the collected vibration signal to obtain an appropriate envelope spectrum, and the entropy of the envelope spectrum is calculated. Then, we use the PCA method to calculate the contribution rate of each characteristic value,and the feature values with a cumulative contribution rate higher than 90% are used as the final feature vector . Finally , use the WOA algorithm to optimize the penalty factors and kernel parameters of SVM. Through parameter optimization and feature optimization,it can be found that the WOA-SVM was superior to GA-SVM for optimized time and accuracy, providing an effective method for fault diagnosis of the circuit breaker. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： HVCB Vibration signal Principal component analysis whale optimization algorithm

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Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids

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SOLAR ENERGY 2023年第1期251卷 306-314页

作者： Li, Yihong Tao, Qi Gong, Yadong Zhaoqing Univ Sch Comp Sci & Software Guangdong 526000 Peoples R China South China Normal Univ Sch Data Sci & Engn Guangdong 516600 Peoples R China

Recent attention is focused on interlinked (networked) hybrid connected microgrids (MGs) because of their technical and economic benefits over traditional ones. A system like that would connect the DC resources, such as solar energy, to DC busses and the AC resources, such as wind turbines, to AC busses. Therefore, the system's overall performance is remarkably improved. In addition, it is cost-effective compared to traditional grids since no power electronics are required to convert DC to AC. While a system like this has numerous benefits, it also has numerous challenges. System-level energy management (EM), for example, presents greater challenges because each change in the subsystem (DC/AC sections) may potentially alter the EM for the whole grid. Therefore, the present study develops the whale optimization algorithm (WOA) for finding the optimum solution. Additionally, blockchain (BC) technology has been applied for increasing the security of power trading in hybrid MGs (HMGs). Stochastic load flow, according to a copula model, is used to create a realistic layout. According to the results, the suggested networked HMG-EM structure based on BC is demonstrated as being effective.

关键词： whale optimization algorithm Modified blockchain Hybrid connected microgrid Security enhancement Uncertainty

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Humanoid Robot Vehicle Driving Navigation Based on Monocular Vision

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ENGINEERING LETTERS 2025年第5期33卷 1267-1281页

作者： Chen, Dongdong Wei, Yuan Zhou, Zhigang Li, Zongwen Chen, Peiyuan Henan Univ Sci & Technol Coll Vehicle & Traff Engn Luoyang 471000 Peoples R China

This paper presents a novel approach aimed at enabling humanoid robots to navigate by utilizing vision for obstacle avoidance. A distance prediction model is proposed that incorporates visual recognition and range algorithms, as well as an optimized BP neural network model refined through an improved whale algorithm (CIWOA-BP). The primary objective is to accurately estimate obstacle distances to establish appropriate steering angles, thereby preventing collisions. The obstacles are identified by visual recognition, and the distance between the robot and obstacles are estimated, and then the distance is mapped to the distance between the car and the obstacle. The neural network prediction model takes distance inputs and wheel angles as outputs. Through extensive training and validation using humanoid driving data, a robust prediction model is successfully developed. The predicted wheel angles are translated into steering wheel and robot joint angles, enabling obstacle avoidance through joint angle adjustments. Experimental validation is conducted using the NAO humanoid robot and a micro electric vehicle within a Linux environment. Real-time vehicle control is achieved by interfacing the NAO's Qi system with the main controller, Jetson Nano. Results demonstrate the effectiveness of the proposed approach, with the distance prediction model exhibiting an error rate of approximately 6%, well within acceptable margins. Comparative studies show that the algorithm proposed in this paper has greater prediction accuracy and generalization capabilities.

关键词： Humanoid robot BP neural network Vision-based navigation whale optimization algorithm Obstacle avoidance

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Emotion recognition in speech signals using optimization based multi-SVNN classifier

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第2期34卷 384-397页

作者： Mannepalli, Kasiprasad Sastry, Panyam Narahari Suman, Maloji KL Univ KLEE Vijayawada 522502 Andhra Pradesh India CBIT Hyderabad 500075 Telangana India

Emotion recognition is an interdisciplinary area, and it achieves a significant attention of the researchers in the past few years. Automatic recognition of an emotional state intends to attain an interface among the machines and human beings. Accordingly, a speaker emotion recognition system, named Fractional Deep Belief Network (FDBN), is designed in the literature using fractional deep belief network that combines fractional theory and deep belief network. This work introduced a novel emotion recognition scheme, whale-Imperialist optimization algorithm (whale-IpCA) based Multiple Support Vector Neural Network (Multi-SVNN) classifier, for identifying the emotions in the speech signal. The newly proposed whale-IpCA algorithm hybridizes the whale optimization algorithm (WOA) and Imperialist Competitive algorithm (IpCA), and it trains the Multi-SVNN classifier for identifying the emotions. Also, the spectral feature set is extracted from the input signal and provided to the proposed whaleIpCA based Multi-SVNN for the recognition purpose. Simulation of the proposed whale-IpCA based Multi-SVNN is done with the help of the standard emotion databases, such as Berlin and Telugu. From the results, it is evident that the proposed whale-IpCA based Multi-SVNN classifier has surpassed other existing works with the values of 0.0025, 0, and 0.9987 for the FNR, FPR, and accuracy, respectively. (c) 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Emotion recognition Speech signal Multi-SVNN whale optimization algorithm Imperialist Competitive algorithm

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Energy-efficiency optimization and the comparative performance analysis for Wireless Body Area Networks (WBANs)

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2024年 42卷

作者： Arora, Neha Gupta, Sindhu Hak Kumar, Basant Amity Univ Amity Inst Def Technol Noida Uttar Pradesh India Amity Univ Dept Elect & Commun Engn Noida Uttar Pradesh India MNNIT Dept Elect & Commun Engn Allahabad Uttar Pradesh India

This paper examines the energy efficiency of a non-cooperative and cooperative On-body Wireless Body Area Network (WBAN) and link reliability in a cooperative WBAN using IEEE 802.15.6 based CM3A channel model. The proposed energy optimization framework is based on whale optimization algorithm (WOA) and Particle Swarm optimization (PSO) techniques. For the optimized code rate, obtained results illustrate that WOA gives 44.7%. and 43.4% better results for the non-cooperative and cooperative cases respectively than the PSO. The link reliability has been investigated by observing the effect of the fading parameter (m) on the outage probability over the Nakagami m fading channel. The obtained results reveal how the fading parameter (m) affected the likelihood of an outage.

关键词： communication Energy optimization Particle swarm optimization whale optimization algorithm Wireless body area networks

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Fault diagnosis and prediction of wind turbine gearbox based on a new hybrid model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第9期30卷 24506-24520页

作者： Wang, Haifeng Zhao, Xingyu Wang, Weijun North China Elect Power Univ Dept Econ & Management 689 Huadian Rd Baoding 071003 Peoples R China

Gearbox is an important part of wind turbine. Diagnosing and forecasting gearbox faults of wind turbines can effectively reduce the costs of operation and maintenance and improve the reliability of gearbox operation. Due to high dimensionality and nonlinearity of system parameters, the paper uses the grey relation analysis to select features related to gearbox oil temperature. Features with a relational degree above 0.7 are selected as input data related to oil temperature, including wind speed, ambient temperature, power, and gearbox shaft temperature. Then, a new extreme learning machine with kernel improved by the whale optimization algorithm is established to forecast gearbox oil temperature. Through the residuals between gearbox oil temperature predicted by the proposed model and monitored by the SCADA, whether the gearbox exists faults can be diagnosed. In the case study, the test data was divided into two groups (the test data with and without faults). In the data test without faults, compared with three other models, the proposed model has the smallest false-negative rate (0.211%) and mean absolute percentage error (2.812%). In the data test with faults, the proposed model can diagnose gearbox faults earlier (160 min in advance) than the other three benchmark models. The results show that the proposed hybrid model performs well in the fault diagnosis and prediction of wind turbine gearbox.

关键词： Fault diagnosis and prediction Extreme learning machine with kernel whale optimization algorithm Wind turbine gearbox Statistical process control

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A new demand response management strategy considering renewable energy prediction and filtering technology

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RENEWABLE ENERGY 2023年第1期211卷 656-668页

作者： Zheng, Xidong Bai, Feifei Zhuang, Zhiyuan Chen, Zixing Jin, Tao Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350108 Peoples R China Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Griffith Univ Sch Engn & Built Environm Gold Coast Qld 4222 Australia State Grid Fuzhou Elect Power Supply Co Fuzhou 350009 Peoples R China

Accurate prediction of renewable energy generation acts as a critical role which not only provides short-term power generation in the future, but also facilitates scheduling and pre-configuration of energy storage sys-tems. More importantly, the power generation prediction is of great significance to the demand response man-agement (DRM) of renewable energy to participate in the electricity spot market. Therefore, DRM helps improve the stability and reliability of renewable energy systems. This paper presents a novel prediction-smoothing based methodology to reduce and eliminate the influence caused by the uncertainty of renewable energy output. Firstly, the whale optimization algorithm (WOA) is combined with Long Short-Term Memory (LSTM) to predict short-term wind power output. Then, the Hampel-Butterworth-SG filtering strategy with outlier regression and specific risk band elimination is introduced. After that, according to the short-term output forecast results, the scheduling and pre-configuration scheme of peak-filling type energy storage is developed. Finally, based on the predicted renewable energy output and electricity price, the dynamic changes of demand response (DR) are calculated based on Logistics function, optimistic response and pessimistic response factors. Through extensive case studies, it is demonstrated that the assessment deviations in different scenarios is less than 5%, which are 0.64% for Scenario 1 and 3.64% for Scenario 2, and no additional penalty is required at this time. In addition, the proposed Demand Response Load Adjustment Rate (DRLAR) help compare the differences between predicted and actual DR, which are DRLAR = 0.03% in Scenario 1 and DRLAR = 0.01% in Scenario 2. Users are able to adjust the DR dynamically according to the electricity price to realize the optimal scheduling of their renewable re-sources. The proposed methodology creates a special connection between DRM and renewable energy prediction, which provides a reliable r

关键词： Demand response management Logistics function Short-term wind power prediction whale optimization algorithm Renewable energy smoothing strategy Energy storage system

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