Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate becau...Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.展开更多
In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, ther...In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The Cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.展开更多
Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature prev...Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature previously.Furthermore,there are a number of issues related to time consumed and overall network performance when it comes to big data information.In the existing method,less performed optimization algorithms were used for optimizing the data.In the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was used.The research presents a method for analyzing healthcare information that uses in future prediction.The major goal is to take a variety of data while improving efficiency and minimizing process time.The suggested method employs a hybrid method that is divided into two stages.In the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor operator.In the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and SVM.The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources.For improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog computing.Overall results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.展开更多
Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can hel...Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.展开更多
Layout design problem is to determine a suitable arrangement for the departments so that the total costs associated with the flow among departments become least. Single Row Facility Layout Problem, SRFLP, is one of &l...Layout design problem is to determine a suitable arrangement for the departments so that the total costs associated with the flow among departments become least. Single Row Facility Layout Problem, SRFLP, is one of </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">layout problems that have many practical applications. This problem and its specific scenarios are often used to model many of the raised issues in the field of facility location. SRFLP is an arrangement of </span><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;"> departments with a specified length in a straight line so that the sum of the weighted distances between the pairs of departments is minimized. This problem is NP-hard. In this paper, first, a lower bound for a special case of SRFLP is presented. Then, a general </span><span style="font-family:Verdana;">case of SRFLP is presented in which some new and real assumptions are added to generate more practical model. Then a lower bound, as well as an algorithm, is proposed for solving the model. Experimental results on some instances in literature show the efficiency of our algorithm.展开更多
Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity an...Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.展开更多
In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particl...In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.展开更多
Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the...Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.展开更多
Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where effic...Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where efficient power flow and robust fault handling are crucial for maintaining system reliability.However,current grid-integrated systems face challenges such as inefficient fault management,harmonic distortions,and instability when dealing with nonlinear loads.Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments.Therefore,the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic(SPV)systems with MLUPFCs.Initially,the SPV array generates direct current(DC)power,which is optimized using a perturb and observe maximum power point tracking controller.The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter(VSI)or voltage source converter(VSC).The VSI/VSC,enhanced by greedy control-based monarch butterfly optimization,converts DC to AC while minimizing harmonic distortion.The power is then fed into the grid,which supplies sensitive critical and nonlinear loads.Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions.Furthermore,the ML-UPFC,controlled by a random forest cuckoo search optimization algorithm,enhances the fault ride-through capabilities and power regulation.Additional transformers and a shunt transformer optimize the voltage levels and reactive power management,ensuring stable and high-quality power delivery to both sensitive and nonlinear loads.Finally,the proposed approach addresses power flow optimization,fault mitigation,and nonlinear load management with the aim of enhancing grid stability and efficiency.展开更多
文摘Heart failure prediction is crucial as cardiovascular diseases become the leading cause of death worldwide,exacerbated by the COVID-19 pandemic.Age,cholesterol,and blood pressure datasets are becoming inadequate because they cannot capture the complexity of emerging health indicators.These high-dimensional and heterogeneous datasets make traditional machine learning methods difficult,and Skewness and other new biomarkers and psychosocial factors bias the model’s heart health prediction across diverse patient profiles.Modern medical datasets’complexity and high dimensionality challenge traditional predictionmodels like SupportVectorMachines and Decision Trees.Quantum approaches include QSVM,QkNN,QDT,and others.These Constraints drove research.The“QHF-CS:Quantum-Enhanced Heart Failure Prediction using Quantum CNN with Optimized Feature Qubit Selection with Cuckoo Search in Skewed Clinical Data”system was developed in this research.This novel system leverages a Quantum Convolutional Neural Network(QCNN)-based quantum circuit,enhanced by meta-heuristic algorithms—Cuckoo SearchOptimization(CSO),Artificial BeeColony(ABC),and Particle SwarmOptimization(PSO)—for feature qubit selection.Among these,CSO demonstrated superior performance by consistently identifying the most optimal and least skewed feature subsets,which were then encoded into quantum states for circuit construction.By integrating advanced quantum circuit feature maps like ZZFeatureMap,RealAmplitudes,and EfficientSU2,the QHF-CS model efficiently processes complex,high-dimensional data,capturing intricate patterns that classical models overlook.The QHF-CS model improves precision,recall,F1-score,and accuracy to 0.94,0.95,0.94,and 0.94.Quantum computing could revolutionize heart failure diagnostics by improving model accuracy and computational efficiency,enabling complex healthcare diagnostic breakthroughs.
文摘In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The Cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R161)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features.Several cloud-based IoT health providers have been described in the literature previously.Furthermore,there are a number of issues related to time consumed and overall network performance when it comes to big data information.In the existing method,less performed optimization algorithms were used for optimizing the data.In the proposed method,the Chaotic Cuckoo Optimization algorithm was used for feature selection,and Convolutional Support Vector Machine(CSVM)was used.The research presents a method for analyzing healthcare information that uses in future prediction.The major goal is to take a variety of data while improving efficiency and minimizing process time.The suggested method employs a hybrid method that is divided into two stages.In the first stage,it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight,opposition-based learning,and distributor operator.In the second stage,CSVM is used which combines the benefits of convolutional neural network(CNN)and SVM.The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources.For improved economic flexibility,greater protection,greater analytics with confidentiality,and lower operating cost,the suggested approach is built on fog computing.Overall results of the experiments show that the suggested method can minimize the number of features in the datasets,enhances the accuracy by 82%,and decrease the time of the process.
文摘Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats.Application Programming Interfaces(API)calls contain valuable information that can help with malware identification.The malware analysis with reduced feature space helps for the efficient identification of malware.The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy.Three swarm optimization methods,viz.,Ant Lion Optimization(ALO),Cuckoo Search Optimization(CSO),and Firefly Optimization(FO)are applied to API calls using auto-encoders for identification of most influential features.The nature-inspired wrapperbased algorithms are evaluated using well-known Machine Learning(ML)classifiers such as Linear Regression(LR),Decision Tree(DT),Random Forest(RF),K-Nearest Neighbor(KNN)&SupportVector Machine(SVM).A hybrid Artificial Neuronal Classifier(ANC)is proposed for improving the classification of android malware.The experimental results yielded an accuracy of 98.87%with just seven features out of hundred API call features,i.e.,a massive 93%of data optimization.
文摘Layout design problem is to determine a suitable arrangement for the departments so that the total costs associated with the flow among departments become least. Single Row Facility Layout Problem, SRFLP, is one of </span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">the </span></span></span><span><span><span style="font-family:""><span style="font-family:Verdana;">layout problems that have many practical applications. This problem and its specific scenarios are often used to model many of the raised issues in the field of facility location. SRFLP is an arrangement of </span><i><span style="font-family:Verdana;">n</span></i><span style="font-family:Verdana;"> departments with a specified length in a straight line so that the sum of the weighted distances between the pairs of departments is minimized. This problem is NP-hard. In this paper, first, a lower bound for a special case of SRFLP is presented. Then, a general </span><span style="font-family:Verdana;">case of SRFLP is presented in which some new and real assumptions are added to generate more practical model. Then a lower bound, as well as an algorithm, is proposed for solving the model. Experimental results on some instances in literature show the efficiency of our algorithm.
基金supported by the Yalong River Joint Funds of the National Natural Science Foundation of China(No.U1965207)the National Natural Science Foundation of China(Nos.51839007,51779169,and 52009090)。
文摘Accurate prediction of drilling efficiency is critical for developing the earth-rock excavation schedule.The single machine learning(ML)prediction models usually suffer from problems including parameter sensitivity and overfitting.In addition,the influence of environmental and operational factors is often ignored.In response,a novel stacking-based ensemble learning method taking into account the combined effects of those factors is proposed.Through multiple comparison tests,four models,e Xtreme gradient boosting(XGBoost),random forest(RF),back propagation neural network(BPNN)as the base learners,and support vector regression(SVR)as the meta-learner,are selected for stacking.Furthermore,an improved cuckoo search optimization(ICSO)algorithm is developed for hyper-parameter optimization of the ensemble model.The application to a real-world project demonstrates that the proposed method outperforms the popular single ML method XGBoost and the ensemble model optimized by particle swarm optimization(PSO),with 16.43%and 4.88%improvements of mean absolute percentage error(MAPE),respectively.
基金National Natural Science Foundation of China(Nos.61164010,61233003)。
文摘In view of the shortcomings of traditional Bayesian network(BN)structure learning algorithm,such as low efficiency,premature algorithm and poor learning effect,the intelligent algorithm of cuckoo search(CS)and particle swarm optimization(PSO)is selected.Combined with the characteristics of BN structure,a BN structure learning algorithm of CS-PSO is proposed.Firstly,the CS algorithm is improved from the following three aspects:the maximum spanning tree is used to guide the initialization direction of the CS algorithm,the fitness of the solution is used to adjust the optimization and abandoning process of the solution,and PSO algorithm is used to update the position of the CS algorithm.Secondly,according to the structure characteristics of BN,the CS-PSO algorithm is applied to the structure learning of BN.Finally,chest clinic,credit and car diagnosis classic network are utilized as the simulation model,and the modeling and simulation comparison of greedy algorithm,K2 algorithm,CS algorithm and CS-PSO algorithm are carried out.The results show that the CS-PSO algorithm has fast convergence speed,high convergence accuracy and good stability in the structure learning of BN,and it can get the accurate BN structure model faster and better.
文摘Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.
文摘Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where efficient power flow and robust fault handling are crucial for maintaining system reliability.However,current grid-integrated systems face challenges such as inefficient fault management,harmonic distortions,and instability when dealing with nonlinear loads.Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments.Therefore,the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic(SPV)systems with MLUPFCs.Initially,the SPV array generates direct current(DC)power,which is optimized using a perturb and observe maximum power point tracking controller.The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter(VSI)or voltage source converter(VSC).The VSI/VSC,enhanced by greedy control-based monarch butterfly optimization,converts DC to AC while minimizing harmonic distortion.The power is then fed into the grid,which supplies sensitive critical and nonlinear loads.Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions.Furthermore,the ML-UPFC,controlled by a random forest cuckoo search optimization algorithm,enhances the fault ride-through capabilities and power regulation.Additional transformers and a shunt transformer optimize the voltage levels and reactive power management,ensuring stable and high-quality power delivery to both sensitive and nonlinear loads.Finally,the proposed approach addresses power flow optimization,fault mitigation,and nonlinear load management with the aim of enhancing grid stability and efficiency.
