In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly detection.The effectiveness of deep learning-based methods has been proven in extracting better fea...In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly detection.The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods.In this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the IoT.Also,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection.Finally,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN.In the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score criteria.The proposed CNN-BMEGTO-KNN model has reached 99.99%and 99.86%accuracy on TON-IoT and NSL-KDD datasets,respectively.In addition,the proposed BMEGTO method can identify about 27%and 25%of the effective features of the NSL-KDD and TON-IoT datasets,respectively.展开更多
Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and ...Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.展开更多
In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance...In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.展开更多
When talking about Napoleon,people always focus on his military talent,his empire,his famous code or even his final defeat—Battle of Waterloo.Few people will remember his quest for Egypt and his mysterious‘troop’.T...When talking about Napoleon,people always focus on his military talent,his empire,his famous code or even his final defeat—Battle of Waterloo.Few people will remember his quest for Egypt and his mysterious‘troop’.This thesis will discuss the forgotten troop of Napoleon and its influence on the development of Egypt and the world.展开更多
Objective: to analyze the causes of the grassroots army physical training, and to give targeted research.Methods: the outpatient information registration form of 2019-2020 was selected, and through the data analysis o...Objective: to analyze the causes of the grassroots army physical training, and to give targeted research.Methods: the outpatient information registration form of 2019-2020 was selected, and through the data analysis of training injury cases, the number of patients was 100.A total of 100 training injury patients included 39 cases, 31 cases, 20 cases and 10 cases of organ injury, bone tissue injury, joint injury and other injuries each. By analyzing the reasons, and doing the countermeasure analysis, compare the specific effect. Results: through the analysis of the training injury, 13 cases, 8 cases, 8 cases, 4 cases, 3 cases, t=5.096,7.035,4.832,3.621, p <0.05. The quality of mental, daily life, society, psychological ± 3.3,68.4 ± 2.5,20.2 ± 2.4,68.5 ± 2.2, respectively. The overall situation improved significantly after prevention (p <0.05).By comparing the satisfaction of patients before and after prevention, prevention was prone to training injury, satisfaction was 66.3 ± 3.7 points, training injury significantly decreased, the satisfaction score was 92.2 ± 2.1, with certain differences (t=12.565) p <0.05. Conclusion: analyze the causes of army training injury and give some countermeasures.展开更多
为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite ...为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies,QGGTO)。融合二次插值策略和精英个体遗传策略,促进候选解之间的信息交流以加速收敛,并维持种群遗传多样性以避免局部最优。针对包含规则障碍物和不规则障碍物的复杂地形场景,构建了综合考虑行走距离、安全性和转向角度的成本函数,用于统一评估算法的路径规划性能。实验结果表明:QGGTO整体寻优性能优于GTO等7种竞争算法。在4种复杂障碍环境下,QGGTO能够辅助机器人规划出最接近全局最优的路径,验证了其在实际应用中的有效性。展开更多
Although troop fission is a rare event, it does occur in provisioned or wild troops. Such fission, from the ecological viewpeint, results from the population growth which exceeds the carrying capacity of its ranging a...Although troop fission is a rare event, it does occur in provisioned or wild troops. Such fission, from the ecological viewpeint, results from the population growth which exceeds the carrying capacity of its ranging area, and, from the sociological viewpoint, from the loose social relations among troop members.展开更多
文摘In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly detection.The effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other methods.In this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the IoT.Also,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature selection.Finally,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called CNN-BMEGTO-KNN.In the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score criteria.The proposed CNN-BMEGTO-KNN model has reached 99.99%and 99.86%accuracy on TON-IoT and NSL-KDD datasets,respectively.In addition,the proposed BMEGTO method can identify about 27%and 25%of the effective features of the NSL-KDD and TON-IoT datasets,respectively.
基金This work is financially supported by the Fundamental Research Funds for the Central Universities under Grant 2572014BB06.
文摘Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.
文摘In cloud computing(CC),resources are allocated and offered to the cli-ents transparently in an on-demand way.Failures can happen in CC environment and the cloud resources are adaptable tofluctuations in the performance delivery.Task execution failure becomes common in the CC environment.Therefore,fault-tolerant scheduling techniques in CC environment are essential for handling performance differences,resourcefluxes,and failures.Recently,several intelli-gent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics.With this motivation,this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme(GTO-FTASS)in CC environment.The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account.The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops.Besides,the GTO-FTASS model derives afitness function involving two parameters such as expected time of completion(ETC)and failure probability of executing a task.In addition,the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model.The experimental vali-dation of the GTO-FTASS model has been performed and the results are inspected under several aspects.Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.
文摘When talking about Napoleon,people always focus on his military talent,his empire,his famous code or even his final defeat—Battle of Waterloo.Few people will remember his quest for Egypt and his mysterious‘troop’.This thesis will discuss the forgotten troop of Napoleon and its influence on the development of Egypt and the world.
文摘Objective: to analyze the causes of the grassroots army physical training, and to give targeted research.Methods: the outpatient information registration form of 2019-2020 was selected, and through the data analysis of training injury cases, the number of patients was 100.A total of 100 training injury patients included 39 cases, 31 cases, 20 cases and 10 cases of organ injury, bone tissue injury, joint injury and other injuries each. By analyzing the reasons, and doing the countermeasure analysis, compare the specific effect. Results: through the analysis of the training injury, 13 cases, 8 cases, 8 cases, 4 cases, 3 cases, t=5.096,7.035,4.832,3.621, p <0.05. The quality of mental, daily life, society, psychological ± 3.3,68.4 ± 2.5,20.2 ± 2.4,68.5 ± 2.2, respectively. The overall situation improved significantly after prevention (p <0.05).By comparing the satisfaction of patients before and after prevention, prevention was prone to training injury, satisfaction was 66.3 ± 3.7 points, training injury significantly decreased, the satisfaction score was 92.2 ± 2.1, with certain differences (t=12.565) p <0.05. Conclusion: analyze the causes of army training injury and give some countermeasures.
文摘为解决移动机器人在复杂地形场景的路径规划中易陷入局部最优和收敛速度慢等问题,提出了一种多策略集成的增强型人工大猩猩算法(enhanced artificial gorilla troops optimizer with integration of quadratic interpolation and elite individual genetic strategies,QGGTO)。融合二次插值策略和精英个体遗传策略,促进候选解之间的信息交流以加速收敛,并维持种群遗传多样性以避免局部最优。针对包含规则障碍物和不规则障碍物的复杂地形场景,构建了综合考虑行走距离、安全性和转向角度的成本函数,用于统一评估算法的路径规划性能。实验结果表明:QGGTO整体寻优性能优于GTO等7种竞争算法。在4种复杂障碍环境下,QGGTO能够辅助机器人规划出最接近全局最优的路径,验证了其在实际应用中的有效性。
基金Project supported by the National Natural Science Foundation of China.
文摘Although troop fission is a rare event, it does occur in provisioned or wild troops. Such fission, from the ecological viewpeint, results from the population growth which exceeds the carrying capacity of its ranging area, and, from the sociological viewpoint, from the loose social relations among troop members.
