As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been ...As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.展开更多
Mobile Cloud Computing(MCC)becomes an emerging computing paradigm,where Mobile Devices(MDs)are in the place for offloading task to the nearest resource-rich cloud servers.To promote the system’s performance,the MCC i...Mobile Cloud Computing(MCC)becomes an emerging computing paradigm,where Mobile Devices(MDs)are in the place for offloading task to the nearest resource-rich cloud servers.To promote the system’s performance,the MCC is performed.However,it holds with more overhead complexity in storage and energy,which degrades the network efficiency.Hence the scholar concentrates on decreasing the overhead issue by applying the task offloading process.The major issue in this mechanism is having most cost-effective communication among the devices.This research paper suggests a new optimization strategy for performing the offloading task in MCC.The developed hybrid approach offloads the task to the nearby server to enhance the performance of the MCC by finishing the task within the deadline.A new cost function is derived with the adoption of the average delay of tasks,the energy consumption level,battery lifetime,processing capabilities,storage capacity,response time,communication cost,etc for optimizing the task offloading.Thus,a new task offloading is optimized via a newly recommended hybrid optimizer with the adoption of Probability Condition of Satin Bowerbird Forensic Optimization(PCSBFO),which is developed with the combination of Satin Bowerbird Optimization(SBO)and Forensic-Based Investigation(FBI)to achieve optimal solutions.Additionally,the developed PCSBFO considers the multi-objective constraints such as average delay,energy consumption,and offloading expenditure for ensuring the quality of service,and satisfactory level of the end user in the MCC.This suggested lightweight paradigm addresses the difficulties and minimizes the efforts while developing,deploying,and managing to offload using optimization algorithms to help better available frameworks.Further,the creation of APAs is done to enable the mobile applications to extract maximum utility out of the volumes of available resources.The experiment results show that the suggested hybrid optimization-based task…展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41807192,41790441)Innovation Capability Support Program of Shaanxi(Grant No.2020KJXX-005)Natural Science Basic Research Program of Shaanxi(Grant Nos.2019JLM-7,2019JQ-094)。
文摘As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.
文摘Mobile Cloud Computing(MCC)becomes an emerging computing paradigm,where Mobile Devices(MDs)are in the place for offloading task to the nearest resource-rich cloud servers.To promote the system’s performance,the MCC is performed.However,it holds with more overhead complexity in storage and energy,which degrades the network efficiency.Hence the scholar concentrates on decreasing the overhead issue by applying the task offloading process.The major issue in this mechanism is having most cost-effective communication among the devices.This research paper suggests a new optimization strategy for performing the offloading task in MCC.The developed hybrid approach offloads the task to the nearby server to enhance the performance of the MCC by finishing the task within the deadline.A new cost function is derived with the adoption of the average delay of tasks,the energy consumption level,battery lifetime,processing capabilities,storage capacity,response time,communication cost,etc for optimizing the task offloading.Thus,a new task offloading is optimized via a newly recommended hybrid optimizer with the adoption of Probability Condition of Satin Bowerbird Forensic Optimization(PCSBFO),which is developed with the combination of Satin Bowerbird Optimization(SBO)and Forensic-Based Investigation(FBI)to achieve optimal solutions.Additionally,the developed PCSBFO considers the multi-objective constraints such as average delay,energy consumption,and offloading expenditure for ensuring the quality of service,and satisfactory level of the end user in the MCC.This suggested lightweight paradigm addresses the difficulties and minimizes the efforts while developing,deploying,and managing to offload using optimization algorithms to help better available frameworks.Further,the creation of APAs is done to enable the mobile applications to extract maximum utility out of the volumes of available resources.The experiment results show that the suggested hybrid optimization-based task…
