The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making...The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.展开更多
Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its forma...Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.展开更多
The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow S...The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.展开更多
As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infect...As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach.展开更多
The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial in...The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.展开更多
The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framew...The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framework for healthcare applications that integrates 5G communication,quantum computing,and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data.The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems,ensuring user anonymity and data confidentiality.The framework is applied to innovative healthcare scenarios,such as secure 5G voice communication,data transmission,and short message services.Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems,surpassing existing approaches.展开更多
This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formali...This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical model.Second,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_CS.Then,fourteen local search operators are employed to search for better solutions.Two different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process.Finally,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different problems.The experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned problems.This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.展开更多
As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very litt...As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very little research focusing on distributed scheduling for heterogeneous factories.This study addresses a multi-objective distributed heterogeneous permutation flow shop scheduling problem with sequence-dependent setup times(DHPFSP-SDST).The objective is to optimize the trade-off between the maximum completion time(Makespan)and total energy consumption.First,to describe the concerned problems,we establish a mathematical model.Second,we use the artificial bee colony(ABC)algorithm to optimize the two objectives,incorporating five local search strategies tailored to the problem characteristics to enhance the algorithm's performance.Third,to improve the convergence speed of the algorithm,a Q-learning based strategy is designed to select the appropriated local search operator during iterations.Finally,based on experiments conducted on 72 instances,statistical analysis and discussions show that the Q-learning based ABC algorithm can effectively solve the problems better than its peers.展开更多
Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive attention.This study focuses on solving a surgery scheduling problem with setup time.First a mathematical...Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive attention.This study focuses on solving a surgery scheduling problem with setup time.First a mathematical model is created to minimize the maximum completion time(makespan)of all surgeres and patient waiting time,simultaneously.The time by the fatigue effect is included in the surgery time,which is caused by doctors’long working time.Second,four mate-heuristics are optimized to address the relevant problems.Three novel strategies are designed to improve the quality of the initial solutions.To improve the convergence of the algorithms,seven local search operators are proposed based on the characteristics of the surgery scheduling problems.Third,Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration.Finally,by comparing the experimental results of 30 instances,the Q.learning based local search strategy's effectiveness is verified.Among all the compared algorithms,the improved artificial bee colony(ABC)with Q-leaming based local search has the best competiiveness.展开更多
文摘The rapid and increasing growth in the volume and number of cyber threats from malware is not a real danger;the real threat lies in the obfuscation of these cyberattacks,as they constantly change their behavior,making detection more difficult.Numerous researchers and developers have devoted considerable attention to this topic;however,the research field has not yet been fully saturated with high-quality studies that address these problems.For this reason,this paper presents a novel multi-objective Markov-enhanced adaptive whale optimization(MOMEAWO)cybersecurity model to improve the classification of binary and multi-class malware threats through the proposed MOMEAWO approach.The proposed MOMEAWO cybersecurity model aims to provide an innovative solution for analyzing,detecting,and classifying the behavior of obfuscated malware within their respective families.The proposed model includes three classification types:Binary classification and multi-class classification(e.g.,four families and 16 malware families).To evaluate the performance of this model,we used a recently published dataset called the Canadian Institute for Cybersecurity Malware Memory Analysis(CIC-MalMem-2022)that contains balanced data.The results show near-perfect accuracy in binary classification and high accuracy in multi-class classification compared with related work using the same dataset.
基金This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.
基金partially supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011531)the National Natural Science Foundation of China under Grant 62173356+2 种基金the Science and Technology Development Fund(FDCT),Macao SAR,under Grant 0019/2021/AZhuhai Industry-University-Research Project with Hongkong and Macao under Grant ZH22017002210014PWCthe Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(22JR10KA007).
文摘The flow shop scheduling problem is important for the manufacturing industry.Effective flow shop scheduling can bring great benefits to the industry.However,there are few types of research on Distributed Hybrid Flow Shop Problems(DHFSP)by learning assisted meta-heuristics.This work addresses a DHFSP with minimizing the maximum completion time(Makespan).First,a mathematical model is developed for the concerned DHFSP.Second,four Q-learning-assisted meta-heuristics,e.g.,genetic algorithm(GA),artificial bee colony algorithm(ABC),particle swarm optimization(PSO),and differential evolution(DE),are proposed.According to the nature of DHFSP,six local search operations are designed for finding high-quality solutions in local space.Instead of randomselection,Q-learning assists meta-heuristics in choosing the appropriate local search operations during iterations.Finally,based on 60 cases,comprehensive numerical experiments are conducted to assess the effectiveness of the proposed algorithms.The experimental results and discussions prove that using Q-learning to select appropriate local search operations is more effective than the random strategy.To verify the competitiveness of the Q-learning assistedmeta-heuristics,they are compared with the improved iterated greedy algorithm(IIG),which is also for solving DHFSP.The Friedman test is executed on the results by five algorithms.It is concluded that the performance of four Q-learning-assisted meta-heuristics are better than IIG,and the Q-learning-assisted PSO shows the best competitiveness.
文摘As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the pandemic.Because of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of infections.By analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm optimizers.The lung region was segmented from the original chest X-ray images and augmented using various transformation operations.Furthermore,the augmented images were fed into the VGG19 deep network for feature extraction.On the other hand,a feature selection method is proposed to select the most significant features that can boost the classification results.Finally,the selected features were input into an optimized neural network for detection.The neural network is optimized using the proposed hybrid optimizer.The experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection models.In addition,a deep statistical analysis is performed to study the performance and stability of the proposed optimizer.The results confirm the effectiveness and superiority of the proposed approach.
基金supported by the National Natural Science Foundation of China under Grant No.62202247the National Key R&D Program of China under Grant No.2022ZD0115303.
文摘The metaverse enables immersive virtual healthcare environments,presenting opportunities for enhanced care delivery.A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications,which is a problem that needs to be addressed.This paper proposes a novel multimodal learning framework for metaverse healthcare,MMLMH,based on collaborative intra-and intersample representation and adaptive fusion.Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text,audio,and visual health data.By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms,MMLMH achieves superior feature representation for complex health assessments.The framework’s adaptive fusion approach,utilizing attention mechanisms and gated neural networks,demonstrates robust performance across varying noise levels and data quality conditions.Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics.Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy,patient-system interaction efficacy,and data integration complexity.The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars,which could lead to greater personalization of healthcare experiences in the metaverse.MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources.They can be successfully utilized in next-generation healthcare delivery through virtual reality.
文摘The exponential advancement witnessed in 5G communication and quantum computing has presented unparalleled prospects for safeguarding sensitive data within healthcare infrastructures.This study proposes a novel framework for healthcare applications that integrates 5G communication,quantum computing,and sensitive data measurement to address the challenges of measuring and securely transmitting sensitive medical data.The framework includes a quantum-inspired method for quantifying data sensitivity based on quantum superposition and entanglement principles and a delegated quantum computing protocol for secure data transmission in 5G-enabled healthcare systems,ensuring user anonymity and data confidentiality.The framework is applied to innovative healthcare scenarios,such as secure 5G voice communication,data transmission,and short message services.Experimental results demonstrate the framework’s high accuracy in sensitive data measurement and enhanced security for data transmission in 5G healthcare systems,surpassing existing approaches.
基金supported by the National Natural Science Foundation of China(No.62173356)Science and Technology Development Fund(FDCT),Macao SAR(No.0019/2021/A)+2 种基金Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011531)Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(No.22JR10KA007).
文摘This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy consumption.First,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical model.Second,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_CS.Then,fourteen local search operators are employed to search for better solutions.Two different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration process.Finally,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different problems.The experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned problems.This study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.
基金supported by the Science and Technology Development Fund(FDCT),Macao SAR(No.0019/2021/A)National Natural Science Foundation of China(No.62173356)+2 种基金Zhuhai Industry-University-Research Project with Hongkong and Macao(No.ZH22017002210014PWC)Guangdong Basic and Applied Basic Research Foundation(No.2023A1515011531)Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(No.22JR10KA007).
文摘As the global economy develops and people's awareness of environmental protection increases,the efficient scheduling of production lines in workshops has received more and more attention.However,there is very little research focusing on distributed scheduling for heterogeneous factories.This study addresses a multi-objective distributed heterogeneous permutation flow shop scheduling problem with sequence-dependent setup times(DHPFSP-SDST).The objective is to optimize the trade-off between the maximum completion time(Makespan)and total energy consumption.First,to describe the concerned problems,we establish a mathematical model.Second,we use the artificial bee colony(ABC)algorithm to optimize the two objectives,incorporating five local search strategies tailored to the problem characteristics to enhance the algorithm's performance.Third,to improve the convergence speed of the algorithm,a Q-learning based strategy is designed to select the appropriated local search operator during iterations.Finally,based on experiments conducted on 72 instances,statistical analysis and discussions show that the Q-learning based ABC algorithm can effectively solve the problems better than its peers.
基金supported by the National Natural Science Foundation of China under Grant 62173356the Science and Technology Development Fund(FDCT),Macao,China,under Grant 0019/2021/A,Zhuhai Industry-University-Research Project with Hong Kong and Macao under Grant ZH22017002210014PWC,the Guangdong Basic and Applied Basic Research Foundation(2023A1515011531)research on the Key Technologies for Scheduling and Optimization of Complex Distributed Manufacturing Systems(22JR10KA007).
文摘Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive attention.This study focuses on solving a surgery scheduling problem with setup time.First a mathematical model is created to minimize the maximum completion time(makespan)of all surgeres and patient waiting time,simultaneously.The time by the fatigue effect is included in the surgery time,which is caused by doctors’long working time.Second,four mate-heuristics are optimized to address the relevant problems.Three novel strategies are designed to improve the quality of the initial solutions.To improve the convergence of the algorithms,seven local search operators are proposed based on the characteristics of the surgery scheduling problems.Third,Q-learning is used to dynamically choose the optimal local search operator for the current state in each iteration.Finally,by comparing the experimental results of 30 instances,the Q.learning based local search strategy's effectiveness is verified.Among all the compared algorithms,the improved artificial bee colony(ABC)with Q-leaming based local search has the best competiiveness.
