Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapid...Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.展开更多
Pulmonary Hypertension(PH)is a global health problem that affects about 1%of the global population.Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease.The present study...Pulmonary Hypertension(PH)is a global health problem that affects about 1%of the global population.Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease.The present study proposes a Kernel Extreme Learning Machine(KELM)model based on an improved Whale Optimization Algorithm(WOA)for predicting PH mouse models.The experimental results showed that the selected blood indicators,including Haemoglobin(HGB),Hematocrit(HCT),Mean,Platelet Volume(MPV),Platelet distribution width(PDW),and Platelet–Large Cell Ratio(P-LCR),were essential for identifying PH mouse models using the feature selection method proposed in this paper.Remarkably,the method achieved 100.0%accuracy and 100.0%specificity in classification,demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.展开更多
An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challe...An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.展开更多
Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detec...Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.展开更多
Effective technology for wind direction forecasting can be realized using the recent advances in machine learning.Consequently,the stability and safety of power systems are expected to be significantly improved.Howeve...Effective technology for wind direction forecasting can be realized using the recent advances in machine learning.Consequently,the stability and safety of power systems are expected to be significantly improved.However,the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem.This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models.This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input features.The conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight hours.The recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind direction.In addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its superiority.Moreover,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.展开更多
文摘Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ability.While,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space complexity.In ELM,the hidden layer typically necessitates a huge number of nodes.Furthermore,there is no certainty that the arrangement of weights and biases within the hidden layer is optimal.To solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization techniques.This paper displays five proposed hybrid Algorithms“Salp Swarm Algorithm(SSA-ELM),Grasshopper Algorithm(GOA-ELM),Grey Wolf Algorithm(GWO-ELM),Whale optimizationAlgorithm(WOA-ELM)andMoth Flame Optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression data.The proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear period.In the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of ELM.The best weights and preferences were calculated by these algorithms for the hidden layer.Experimental results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression data.While in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
基金the National Natural Science Foundation of China(82003831,62076185 and U1809209)the Project of Health Commission of Zhejiang Province(2020KY177)+2 种基金the Wenzhou Technology Foundation(Y2020002)the Natural Science Foundation of Zhejiang Province(LZ22F020005)the First Affiliated Hospital of Wenzhou Medical University Youth Excellence Project(QNYC114).
文摘Pulmonary Hypertension(PH)is a global health problem that affects about 1%of the global population.Animal models of PH play a vital role in unraveling the pathophysiological mechanisms of the disease.The present study proposes a Kernel Extreme Learning Machine(KELM)model based on an improved Whale Optimization Algorithm(WOA)for predicting PH mouse models.The experimental results showed that the selected blood indicators,including Haemoglobin(HGB),Hematocrit(HCT),Mean,Platelet Volume(MPV),Platelet distribution width(PDW),and Platelet–Large Cell Ratio(P-LCR),were essential for identifying PH mouse models using the feature selection method proposed in this paper.Remarkably,the method achieved 100.0%accuracy and 100.0%specificity in classification,demonstrating that our method has great potential to be used for evaluating and identifying mouse PH models.
基金supported by the Action Programme for Cultivation of Young and Middle-aged Teachers in Universities in Anhui Province(YQYB2023030),Chinathe Supporting Programme for Outstanding Young Talents in Colleges and Universities of Anhui Provincial Department of Education(gxyq2022068),China+1 种基金the Huainan Normal University Scientific Research Project(2023XJZD016),Chinathe Key Projects of Huainan Normal University(2024XJZD012),China.
文摘An accurate assessment of the state of health(SOH)is the cornerstone for guaranteeing the long-term stable operation of electrical equipment.However,the noise the data carries during cyclic aging poses a severe challenge to the accuracy of SOH estimation and the generalization ability of the model.To this end,this paper proposed a novel SOH estimation model for lithium-ion batteries that incorporates advanced signal-processing techniques and optimized machine-learning strategies.The model employs a whale optimization algorithm(WOA)to seek the optimal parameter combination(K,α)for the variational modal decomposition(VMD)method to ensure that the signals are accurately decomposed into different modes representing the SOH of batteries.Then,the excellent local feature extraction capability of the convolutional neural network(CNN)was utilized to obtain the critical features of each modal of SOH.Finally,the support vector machine(SVM)was selected as the final SOH estimation regressor based on its generalization ability and efficient performance on small sample datasets.The method proposed was validated on a two-class publicly available aging dataset of lithium-ion batteries containing different temperatures,discharge rates,and discharge depths.The results show that the WOA-VMD-based data processing technique effectively solves the interference problem of cyclic aging data noise on SOH estimation.The CNN-SVM optimized machine learning method significantly improves the accuracy of SOH estimation.Compared with traditional techniques,the fused algorithm achieves significant results in solving the interference of data noise,improving the accuracy of SOH estimation,and enhancing the generalization ability.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343)PrincessNourah bint Abdulrahman University,Riyadh,Saudi ArabiaDeanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this researchwork through the project number“NBU-FFR-2024-1092-02”.
文摘Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.
文摘Effective technology for wind direction forecasting can be realized using the recent advances in machine learning.Consequently,the stability and safety of power systems are expected to be significantly improved.However,the unstable and unpredictable qualities of the wind predict the wind direction a challenging problem.This paper proposes a practical forecasting approach based on the weighted ensemble of machine learning models.This weighted ensemble is optimized using a whale optimization algorithm guided by particle swarm optimization(PSO-Guided WOA).The proposed optimized weighted ensemble predicts the wind direction given a set of input features.The conducted experiments employed the wind power forecasting dataset,freely available on Kaggle and developed to predict the regular power generation at seven wind farms over forty-eight hours.The recorded results of the conducted experiments emphasize the effectiveness of the proposed ensemble in achieving accurate predictions of the wind direction.In addition,a comparison is established between the proposed optimized ensemble and other competing optimized ensembles to prove its superiority.Moreover,statistical analysis using one-way analysis of variance(ANOVA)and Wilcoxon’s rank-sum are provided based on the recorded results to confirm the excellent accuracy achieved by the proposed optimized weighted ensemble.
