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Multi head self-attention gated graph convolutional network based multi-attack intrusion detection in MANET

COMPUTERS & SECURITY 2024年 136卷

作者： Reka, R. Karthick, R. Ram, R. Saravana Singh, Gurkirpal Mahendra Coll Engn Dept Artificial Intelligence & Data Sci Salem Campus Salem 636106 Tamil Nadu India KLN Coll Engn Dept Comp Sci & Engn Sivagangai 630612 Tamil Nadu India Anna Univ Dept Elect & Commun Engn Reg Campus Madurai Madurai Tamil Nadu India PTU Architecture Dept Chandigarh 160014 India

Designing of intrusion detection system (IDS), and mobile ad hoc networks (MANET) prevention technique with examined detection rate, memory consumption with minimum overhead are vital concerns. Node mobility and node energy are the two optimization problems in MANETs wherein nodes travel uncertainly in any direction, evolving in a continuous change of topology. With the proposed approach, a Centrality coati optimization algorithm based Cluster Gradient for multi attack intrusion identification is devised. This study focuses on the problems of node mobility and energy to develop a clustering algorithm for cluster head selection in MANET that is incited by Dual Network Centrality. Compact cluster formation is carried out by coati optimization algorithm (COA). The Multi-head Self-Attention based Gated Graph Convolutional Network (MSA-GCNN) with a hybrid type of IDS recognizes several attacks, including DoS and Zero-Day attacks. The proposed technique is implemented in NS-2 network simulator. The performance of proposed approach is examined under some parameters, like attack detection rate, memory consumption, computational time for detecting the intruder. The outcomes display that the proposed technique decreases the IDS traffic and entire consumption of memory and sustains a higher attack identification rate with less computational time. The proposed technique attains 4.299 %, 10.375 % and 6.935 % Accuracy, 5.262 %, 8.375 % and 7.945 % Precision, 7.282 %, 10.365 % and 5.935 % Recall, 9.272 %, 5.355 % and 8.965 % ROC is higher compared with the existing methods such as, Epsilon Swarm Optimized Cluster Gradient along deep belief classifier for multiple attack intrusion detection (ESOC-MA-IDMANET), Intrusion Detection secure solution for intrusion detection in cloud computing utilizing hybrid deep learning approach called EOS-IDS and improved heap optimization (IHO-MA-ID-MANET) for induction detection technique respectively.

关键词： Intrusion Detection MANET coati optimization algorithm The constitutive artificial neural network Mobility

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Generative adversarial network for Multimodal Contrastive Domain Sharing based on efficient invariant feature-centric growth analysis improved brain tumor classification

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ELECTROMAGNETIC BIOLOGY AND MEDICINE 2024年第4期43卷 205-219页

作者： Panyala, Amarendra Reddy Manickam, Baskar Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Hyderabad India SRM Inst Sci & Technol Coll Engn & Technol Sch Comp Dept Comp Technol Kattankulathur Campus Chengalpattu 603203 Tamilnadu India

Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this ***, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range - Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, coati optimization algorithm (COA) optimizes MCDS-GNN's weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively. The proposed MCDS-GNN-IBTC-CGA method starts by cleaning brain tumor images with RDMF and extracting f

关键词： Brain tumor classification brain tumor dataset MRI images Range-Doppler Matched Filter Ternary Pattern and Discrete Wavelet Transforms Multimodal Contrastive Domain Sharing Generative Adversarial Network coati optimization algorithm

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Intelligent Diagnosis of Rolling Bearing Based on ICEEMDAN-WTD of Noise Reduction and Multi-Strategy Fusion optimization SCNs

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IEEE ACCESS 2024年 12卷 36908-36923页

作者： Li, Kun Wu, Hao Liu, Xinming Xu, Jianing Han, Ying Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Peoples R China

Aiming at the problem of noise interference leading to poor fault diagnosis effect of rolling bearing, a two-stage signal noise reduction method based on multi-strategy coati optimization algorithm (MFCOA) optimized ICEEMDAN combined with wavelet threshold denoising (ICEEMDAN-WTD) is proposed. The MFCOA-optimized stochastic configured networks (MFCOA-SCNs) is used for fault type identification. Firstly, ICEEMDAN is used to decompose noisy signals into several IMF signals, and then a wavelet threshold denoising method is used to process IMF signals whose arrangement entropy is lower than the pre-set value to obtain reconstructed signals. In this process, the ICEEMDAN decomposition is affected by the number of white noise additions and the degree of addition. Therefore, MFCOA is introduced to optimize the parameters to ensure the effect of signal noise reduction. Secondly, ICEEMDAN is applied to the reconstructed signal, and the entropy of IMF signal samples of each order is calculated to obtain the fault characteristics in different simultaneous frequency domains. Finally, the scale factor $\lambda $ and regularization coefficient $r$ in SCNs are optimized by MFCOA, and the sample entropy feature is used as the input of the MFCOA-SCNs model to achieve fault identification. Several simulation experiments have verified that the proposed method can effectively reduce the impact of noise in bearing signals, and the experimental results are better than other methods, and the diagnostic accuracy is better.

关键词： coati optimization algorithm ICCEMDAN wavelet threshold denoising stochastic configuration networks parameter optimization

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Research on learning and evaluation model of traditional sports skills of ethnic minorities using optimized spiking deep residual network

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART P-JOURNAL OF SPORTS ENGINEERING AND TECHNOLOGY 2024年

作者： Wang, Jiacheng He, Zhongliang Zhang, Binggang China Agr Univ Dept Phys Educ Beijing 100193 Peoples R China

The athletic performance of competitors in sporting events is influenced by a range of parameters. It is challenging for coaches to develop specialized, scientific training programs for athletes' problems because the standard sports performance prediction approach makes it impossible to obtain precise findings and the related data analysis promptly. A sports performance prediction using the Spiking Deep Residual Network based coati optimization algorithm (SPP-SDRN-COA) is proposed in this manuscript. Initially, the input data was collected from the historical data of ethnic minority athletics. Afterward, data was fed to pre-processing. In pre-processing, the Domain Transform Filtering (DTF) method removed noise by using DTF. Next, the pre-processed data was given to the Spiking Deep Residual Network (SDRN) which classified the sports performance of an athlete's strengths and weakness. In general, SDRN does not show any optimization adaption methods to determine the optimum parameter to offer an accurate prediction. The coati optimization algorithm (COA) is proposed in this manuscript to optimize the SDRN classifier that detects the strengths and weaknesses of the sports performance in an accurate manner. The proposed SPP - SDRN - COA is implemented using the MATLAB platform. By using the proposed SPP - SDRN - COA based sports performance prediction, the method provides higher accuracy and better prediction. Additionally, coaches can analyze athletic performance and gain a more thorough understanding of athletes' strength levels than using the conventional performance prediction method.

关键词： Athlete's performance traditional sports ethnic minority deep spline neural networks coati optimization algorithm MATLAB trials data practice culture

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Safety Status Prediction Model of Transmission Tower Based on Improved coati optimization-Based Support Vector Machine

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BUILDINGS 2024年第12期14卷 3815-3815页

作者： Gong, Xinxi Zhu, Yaozhong Wang, Yanhai Li, Enyang Zhang, Yuhao Zhang, Zilong China Three Gorges Univ Coll Elect & New Energy Yichang 443002 Peoples R China China Three Gorges Univ Hubei Prov Res Ctr Transmiss Line Engn Technol Yichang 443002 Peoples R China Chongqing Elect Power Co State Grid Corp China Ultra High Voltage Branch Chongqing 400039 Peoples R China

Natural calamities have historically impacted operational mountainous power transmission towers, including high winds and ice accumulation, which can result in pole damage or diminished load-bearing capability, compromising their structural integrity. Consequently, developing a safety state prediction model for transmission towers may efficiently monitor and evaluate potential risks, providing early warnings of structural dangers and diminishing the likelihood of bending or collapse incidents. This paper presents a safety state prediction model for transmission towers utilizing improved coati optimization-based SVM (ICOA-SVM). Initially, we optimize the coati optimization algorithm (COA) through inverse refraction learning and Levy flight strategy. Subsequently, we employ the improved coati optimization algorithm (ICOA) to refine the penalty parameters and kernel function of the support vector machine (SVM), thereby developing the safety state prediction model for the transmission tower. A finite element model is created to simulate the dynamic reaction of the transmission tower under varying wind angles and loads;ultimately, wind speed, wind angle, and ice cover thickness are utilized as inputs to the model, with the safe condition of the transmission tower being the output. The predictive outcomes indicate that the proposed ICOA-SVM model exhibits rapid convergence and high prediction accuracy, with a 62.5% reduction in root mean square error, a 59.6% decrease in average relative error, and a 75.0% decline in average absolute error compared to the conventional support vector machine. This work establishes a scientific foundation for the safety monitoring and maintenance of transmission towers, effectively identifying possible dangers and substantially decreasing the likelihood of accidents.

关键词： structural safety stability of buildings transmission towers coati optimization algorithm refractive inverse learning Levy flight support vector machines

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Interpretable real-time monitoring of short-term rockbursts in underground spaces based on microseismic activities

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Kadkhodaei, Mohammad Hossein Ghasemi, Ebrahim Isfahan Univ Technol Dept Min Engn Esfahan *** Iran

In this study, two novel hybrid intelligent models were developed to evaluate the short-term rockburst using the random forest (RF) method and two meta-heuristic algorithms, whale optimization algorithm (WOA) and coati optimization algorithm (COA), for hyperparameter tuning. Real-time predictive models of this phenomenon were created using a database comprising 93 case histories, taking into account various microseismic parameters. The results indicated that the WOA achieved the highest overall performance in hyperparameter tuning for the RF model, outperforming the COA. RF-WOA model accurately predicted the occurrence of this phenomenon with an accuracy of 0.944. Additionally, for this model, precision, recall and F1-score were obtained as 0.950, 0.944 and 0.943, respectively, indicating that the proposed model is robust in predicting damage severity of rockburst in deep underground projects. Subsequently, the Shapley additive explanations (SHAP) method was employed to interpret and explain the prediction process and assess the influence of input features based on RF-WOA model. The results showed that three parameters including cumulative seismic energy, cumulative microseismic events, and cumulative apparent volume have the greatest impact on the occurrence of rockburst events. This study provides an interpretable and transparent resource for accurately predicting rockburst events in real time. It can facilitate estimating project costs, selecting a suitable support system, and identifying essential ways to limit the danger of rockburst.

关键词： Short-term rockburst Microseismic monitoring Random forest Whale optimization algorithm coati optimization algorithm

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Predicting Rock Burst in Underground Engineering Leveraging a Novel Metaheuristic-Based LightGBM Model

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 229-253页

作者： Kai Wang Biao He Pijush Samui Jian Zhou No.Three Engineering Co. Ltd.CCCC First Highway Engineering CompanyBeijing101102China Department of Civil Engineering Faculty of EngineeringUniversiti MalayaKuala Lumpur50603Malaysia Department of Civil Engineering National Institute of Technology PatnaBihar800005India School of Resources and Safety Engineering Central South UniversityChangsha410083China

Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human *** sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural ***,the development of reliable prediction models for rock bursts is paramount to mitigating these *** study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the *** population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM ***,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock *** results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM *** developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,*** sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.

关键词： Rock burst prediction LightGBM coati optimization algorithm pelican optimization algorithm partial dependence plot

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Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms

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International Journal of Digital Earth 2023年第1期16卷 3384-3416页

作者： Zhuo Chen Danqing Song College of Civil Engineering Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China Sichuan Higher Education Engineering Research Center for Disaster Prevention and Mitigation of Village Construction Sichuan Agricultural UniversityDujiangyanPeople’s Republic of China School of Civil Engineering and Transportation South China University of TechnologyGuangzhouPeople’s Republic of China State Key Laboratory of Subtropical Building Science South China University of TechnologyGuangzhouPeople’s Republic of China

Landslides are one of the most common geological hazards worldwide,especially in Sichuan Province(Southwest China).The current study's main,purposes are to explore the potential applications of convolutional neural networks(CNN)hybrid ensemble metaheuristic optimization algorithms,namely beluga whale optimization(BWO)and coati optimization algorithm(COA),for landslide susceptibility mapping in Sichuan Province(China).For this aim,fourteen landslide conditioning factors were compiled in a spatial *** effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine *** receiver operating characteristic(ROC)curve(AUC),the root mean square error,and six statistical indices were used to test and compare the three resultant *** the training dataset,the AUC values of the CNN-COA,CNN-BWO and CNN models were 0.946,0.937 and 0.855,*** terms of the validation dataset,the CNN-COA model exhibited a higher AUC value of 0.919,while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805,*** results indicate that the CNN-COA model,followed by the CNN-BWO model,and the CNN model,offers the best overall performance for landslide susceptibility analysis.

关键词： Landslide susceptibility convolutional neural network beluga whale optimization coati optimization algorithm hybrid models Sichuan Province

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Data center energy consumption prediction model based on deep neural network BiLSTM 48

Data center energy consumption prediction model based on dee...

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48th Annual IEEE International Computers, Software, and Applications Conference (COMPSAC) - Digital Development for a Better Future

作者： Zhou, Junqiang Wang, Yan Li, JieFeng Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Peoples R China Minist Educ Engn Res Ctr Ecol Big Data Hohhot 010021 Peoples R China

ISBN: (纸本)9798350376975;9798350376968

With the rapid development of big data and artificial intelligence technologies, a variety of high-performance platforms and applications in data centers have significantly increased their energy consumption and operating costs. Energy consumption prediction plays an important role in the optimization and management of energy consumption in data centers. Due to the complexity of power system, there are many complex correlations between various energy consumption parameters and prediction results. In order to improve the accuracy of energy consumption prediction, in this paper, a new prediction model based on BiLSTM is proposed, name SLCOA-BiLSTM (Saltation learning-based coati optimization algorithm and Bi-directional long short-term memory). Aiming at the minimum prediction error, the model adopts the improved coati optimization algorithm (SLCOA) to optimize the BiLSTM network's learning rate, lstm_layers number, number of units and dropout. In the process of optimization, in order to solve the shortcomings of coati optimization algorithm, Sobol sequence initialization and Salation learning are improved. The experiments are carried out based on the real-time energy consumption data of a data center, and the results show that the model is superior to the relative comparison model in terms of prediction error. Since SLCOA-BiLSTM predicts are stable and accurate, it can be used as an effective tool to solve energy consumption problems.

关键词： data center energy consumption prediction model BiLSTM neural network coati optimization algorithm

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Short-Term Forecasting of Wind Power Using CEEMDAN-ICOA-GRU Model 33rd

Short-Term Forecasting of Wind Power Using CEEMDAN-ICOA-GRU ...

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Wu, Yun Zheng, Wei Zhao, Yongbin Yang, Jieming An, Ning Feng, Dan Northeast Elect Power Univ Dept Comp Sci Jilin Jilin Peoples R China State Grid Baicheng Power Supply Co Jilin Jilin Peoples R China State Grid Corp China Northeast Branch Shenyang Liaoning Peoples R China Chaoyang Serv Ctr Ecol & Environm Chaoyang Liaoning Peoples R China

ISBN: (纸本)9783031723551;9783031723568

Accurate wind power prediction plays a vital role in ensuring the safe operation of wind power connected to the grid. To improve the prediction accuracy of wind power, a short-term wind power prediction model (CEEMDAN-ICOA-GRU) based on a Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) combined with improved coati optimization algorithm(ICOA) to optimize Gated Recurrent Unit (GRU) is proposed in this paper. First, CEEMDAN was used to decompose the original wind power data, reduce its volatility, and reduce the lag of the forecast curve. Then, to solve the problem that the traditional coati optimization algorithm(COA) is prone to locally optimal solutions, chaotic sequences are added to improve the initial population distribution to be more uniform. Finally, ICOA is used to optimize the hyperparameters of GRU to obtain the optimal prediction model, which is used to predict different sub-sequences, and the prediction results are superimposed to obtain the final prediction results. To verify the validity of the model, a large number of experiments were conducted using the data set of a wind power plant in Turkey in 2022. The results show that CEEMDAN-ICOA-GRU can effectively improve the accuracy of wind power prediction.

关键词： Wind power forecasting CEEMDAN coati optimization algorithm Gated recurrent unit

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