Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualiz...Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.展开更多
Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communi...Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.展开更多
Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best asp...Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.展开更多
针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提...针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。展开更多
目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络...目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络估算动力电池SOC,解决了普通BP网络估计SOC时遇到的训练时间长、收敛慢、精度较低、易陷入局部最优解的问题;同时提升了全局搜索速度,选取电压和电流为输入变量、SOC为输出变量,根据误差的大小调整神经网络的权值和阈值。仿真结果表明,优化后得到的SOC估计结果误差率控制在1.1%以内,该方法寻优速度快,具有更好的鲁棒性。展开更多
针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly wit...针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning,OMSSBOA)。引入柯西变异对最优蝴蝶个体进行扰动,避免算法陷入局部最优;将改进的樽海鞘群优化算法(salp swarm algorithm,SSA)嵌入到BOA,平衡算法全局勘探和局部开采的比重,进而提高算法收敛速度;利用趋优变异反向学习策略扩大算法搜索范围并提升解的质量,进而提高算法的寻优精度。将改进算法在10种基准测试函数上进行仿真实验,结果表明,改进算法具有较好的寻优性能和鲁棒性。展开更多
文摘Cloud computing technology provides flexible,on-demand,and completely controlled computing resources and services are highly desirable.Despite this,with its distributed and dynamic nature and shortcomings in virtualization deployment,the cloud environment is exposed to a wide variety of cyber-attacks and security difficulties.The Intrusion Detection System(IDS)is a specialized security tool that network professionals use for the safety and security of the networks against attacks launched from various sources.DDoS attacks are becoming more frequent and powerful,and their attack pathways are continually changing,which requiring the development of new detection methods.Here the purpose of the study is to improve detection accuracy.Feature Selection(FS)is critical.At the same time,the IDS’s computational problem is limited by focusing on the most relevant elements,and its performance and accuracy increase.In this research work,the suggested Adaptive butterfly optimization algorithm(ABOA)framework is used to assess the effectiveness of a reduced feature subset during the feature selection phase,that was motivated by this motive Candidates.Accurate classification is not compromised by using an ABOA technique.The design of Deep Neural Networks(DNN)has simplified the categorization of network traffic into normal and DDoS threat traffic.DNN’s parameters can be finetuned to detect DDoS attacks better using specially built algorithms.Reduced reconstruction error,no exploding or vanishing gradients,and reduced network are all benefits of the changes outlined in this paper.When it comes to performance criteria like accuracy,precision,recall,and F1-Score are the performance measures that show the suggested architecture outperforms the other existing approaches.Hence the proposed ABOA+DNN is an excellent method for obtaining accurate predictions,with an improved accuracy rate of 99.05%compared to other existing approaches.
基金funded by the Deanship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number:IFP22UQU4281768DSR145.
文摘Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with others.Eye tracking(ET)has become a useful method to detect ASD.One vital aspect of moral erudition is the aptitude to have common visual attention.The eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early detection.Eye-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD detection.It operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with autism.The purpose of this research is to use deep learning to identify autistic disorders based on eye tracking.The Chaotic Butterfly Optimization technique is used to identify this specific disturbance.Therefore,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic Butterfly Optimization with Deep Learning(ETASD-CBODL)technique.The presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL models.To accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested AREASS.In addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter optimization.Finally,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of ASD.To assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data repository.The experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better performance in the ETASD-CBODL technique over recent approaches.
基金This work was supported in part by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2016R1C1B1014069)in part by the National Research Foundation of Korea(NRF)funded by the Korea government(MIST)(No.2021R1A2C1013150).
文摘Since the introduction of the Internet of Things(IoT),several researchers have been exploring its productivity to utilize and organize the spectrum assets.Cognitive radio(CR)technology is characterized as the best aspirant for wireless communications to augment IoT competencies.In the CR networks,secondary users(SUs)opportunistically get access to the primary users(PUs)spectrum through spectrum sensing.The multipath issues in the wireless channel can fluster the sensing ability of the individual SUs.Therefore,several cooperative SUs are engaged in cooperative spectrum sensing(CSS)to ensure reliable sensing results.In CSS,security is still a major concern for the researchers to safeguard the fusion center(FC)against abnormal sensing reports initiated by the malicious users(MUs).In this paper,butterfly optimization algorithm(BOA)-based soft decision method is proposed to find an optimized weighting coefficient vector correlated to the SUs sensing notifications.The coefficient vector is utilized in the soft decision rule at the FC before making any global decision.The effectiveness of the proposed scheme is compared for a variety of parameters with existing schemes through simulation results.The results confirmed the supremacy of the proposed BOA scheme in both the normal SUs’environment and when lower and higher SNRs information is carried by the different categories of MUs.
文摘针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法陷入局部极值不能找到最大功率点(Maximum Power Point,MPP)以及传统的蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)存在收敛速度慢和搜索震荡较大等问题,提出一种改进的蝴蝶优化算法(Improved Butterfly Optimization Algorithm,IBOA)结合电导增量法(Conductance Increment Method,INC)的复合MPPT追踪方法。在IBOA中,引入自适应动态转换概率来平衡算法的全局与局部搜索,然后在全局搜索阶段引入Levy飞行策略,使蝴蝶个体广泛分布于搜索空间中,提高全局寻优能力;同时在局部搜索中设置新的寻优对象,并通过贪婪算法进行筛选保留,提高局部搜索的能力。当系统位于MPP附近时,利用INC局部搜索能力强的优点快速、准确地收敛到MPP并且稳定功率的输出。仿真结果表明,在静态和动态阴影下与BOA、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
文摘目前电动汽车动力输出的来源主要是动力电池,其荷电状态(State of Charge,SOC)表示电池的剩余电量情况,精确估算SOC对于电池的使用安全有重要意义。将蝴蝶优化算法(Butterfly Optimization Algorithm,BOA)进行改进并用于优化BP神经网络估算动力电池SOC,解决了普通BP网络估计SOC时遇到的训练时间长、收敛慢、精度较低、易陷入局部最优解的问题;同时提升了全局搜索速度,选取电压和电流为输入变量、SOC为输出变量,根据误差的大小调整神经网络的权值和阈值。仿真结果表明,优化后得到的SOC估计结果误差率控制在1.1%以内,该方法寻优速度快,具有更好的鲁棒性。
文摘针对蝴蝶优化算法(butterfly optimization algorithm,BOA)易陷入局部最优,且收敛速度慢和寻优精度低等问题,提出了一种趋优变异反向学习的樽海鞘群与蝴蝶混合优化算法(hybrid optimization algorithm for salp swarm and butterfly with reverse mutation towards optimization learning,OMSSBOA)。引入柯西变异对最优蝴蝶个体进行扰动,避免算法陷入局部最优;将改进的樽海鞘群优化算法(salp swarm algorithm,SSA)嵌入到BOA,平衡算法全局勘探和局部开采的比重,进而提高算法收敛速度;利用趋优变异反向学习策略扩大算法搜索范围并提升解的质量,进而提高算法的寻优精度。将改进算法在10种基准测试函数上进行仿真实验,结果表明,改进算法具有较好的寻优性能和鲁棒性。
