Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors.Traditional trial-and-error approaches often aggregate multiple models without optimization by r...Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors.Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance.To address these challenges,we propose a novel Squid Game OptimizationDimension Reduction-based Ensemble(SGO-DRE)method for the precise diagnosis of skin diseases.Our approach begins by selecting pre-trained models named MobileNetV1,DenseNet201,and Xception for robust feature extraction.These models are enhanced with dimension reduction blocks to improve efficiency.To tackle the aggregation problem of various models,we leverage the Squid Game Optimization(SGO)algorithm,which iteratively searches for the optimal weightage set to assign the appropriate weightage to each individual model within the proposed weighted average aggregation ensemble approach.The proposed ensemble method effectively utilizes the strengths of each model.We evaluated the proposed method using an 8-class skin disease dataset,a 6-class MSLD dataset,and a 4-class MSID dataset,achieving accuracies of 98.71%,96.34%,and 93.46%,respectively.Additionally,we employed visual tools like Grad-CAM,ROC curves,and Precision-Recall curves to interpret the decision making of models and assess its performance.These evaluations ensure that the proposed method not only provides robust results but also enhances interpretability and reliability in clinical decision-making.展开更多
Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years.Both Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective ident...Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years.Both Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of cyberattacks.In addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification models.In this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)Model.The goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon data.Besides,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform format.In addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of cyberattacks.Moreover,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.展开更多
In this work,chaos game optimization(CGO),a robust optimization approach,is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems(IPSs)to tackle load-frequ...In this work,chaos game optimization(CGO),a robust optimization approach,is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems(IPSs)to tackle load-frequency con-trol(LFC)difficulties.The CGO method is based on chaos theory principles,in which the structure of fractals is seen via the chaotic game principle and the fractals’self-similarity characteristics are considered.CGO is applied in LFC studies as a novel application,which reveals further research gaps to be filled.For practical implementation,it is also highly desirable to keep the controller structure simple.Accordingly,in this paper,a CGO-based controller of fractional-order(FO)proportional-integral-derivative-FO proportional-integral(FOPID-FOPI)controller is proposed,and the integral time multiplied absolute error performance function is used.Initially,the proposed CGO-based FOPID-FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit.This is a common test system in the literature.A two-area multi-unit system with reheater-hydro-gas in both areas is implemented.To further generalize the advantages of the proposed scheme,a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed.For each test system,comparisons with relevant existing studies are performed.These demonstrate the superiority of the proposed scheme in reducing settling time,and frequency and tie-line power deviations.展开更多
In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to...In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.展开更多
When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game ...When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.展开更多
The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calcula...The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.展开更多
Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted n...Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks.We first model the WVC problem as a general game on weighted networks.Under the framework of a game,we newly define several cover states to describe the WVC problem.Moreover,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the game.Then,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time.Subsequently,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the GAA.Finally,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that...Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.展开更多
The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real...The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.展开更多
Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet hig...Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.展开更多
A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is e...A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is extended to dynamics switching situations to characterize the solutions of this multi-objective problem. Furthermore, the switched differential game is equivalently transformed into a family of parameterized single-objective optimal problems by introducing preference information and auxiliary variables. This transformation reduces the computing complexity such that the Pareto frontier of the switched LQ differential game can be constructed by dynamic programming. Finally, a numerical example is provided to illustrate the effectiveness.展开更多
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R749)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Timely and accurate diagnosis of skin diseases is crucial as conventional methods are time-consuming and prone to errors.Traditional trial-and-error approaches often aggregate multiple models without optimization by resulting in suboptimal performance.To address these challenges,we propose a novel Squid Game OptimizationDimension Reduction-based Ensemble(SGO-DRE)method for the precise diagnosis of skin diseases.Our approach begins by selecting pre-trained models named MobileNetV1,DenseNet201,and Xception for robust feature extraction.These models are enhanced with dimension reduction blocks to improve efficiency.To tackle the aggregation problem of various models,we leverage the Squid Game Optimization(SGO)algorithm,which iteratively searches for the optimal weightage set to assign the appropriate weightage to each individual model within the proposed weighted average aggregation ensemble approach.The proposed ensemble method effectively utilizes the strengths of each model.We evaluated the proposed method using an 8-class skin disease dataset,a 6-class MSLD dataset,and a 4-class MSID dataset,achieving accuracies of 98.71%,96.34%,and 93.46%,respectively.Additionally,we employed visual tools like Grad-CAM,ROC curves,and Precision-Recall curves to interpret the decision making of models and assess its performance.These evaluations ensure that the proposed method not only provides robust results but also enhances interpretability and reliability in clinical decision-making.
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 2/180/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R161)+1 种基金Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4210118DSR07).
文摘Cyberattack detection has become an important research domain owing to increasing number of cybercrimes in recent years.Both Machine Learning(ML)and Deep Learning(DL)classification models are useful in effective identification and classification of cyberattacks.In addition,the involvement of hyper parameters in DL models has a significantly influence upon the overall performance of the classification models.In this background,the current study develops Intelligent Cybersecurity Classification using Chaos Game Optimization with Deep Learning(ICC-CGODL)Model.The goal of the proposed ICC-CGODL model is to recognize and categorize different kinds of attacks made upon data.Besides,ICC-CGODL model primarily performs min-max normalization process to normalize the data into uniform format.In addition,Bidirectional Gated Recurrent Unit(BiGRU)model is utilized for detection and classification of cyberattacks.Moreover,CGO algorithm is also exploited to adjust the hyper parameters involved in BiGRU model which is the novelty of current work.A wide-range of simulation analysis was conducted on benchmark dataset and the results obtained confirmed the significant performance of ICC-CGODL technique than the recent approaches.
文摘In this work,chaos game optimization(CGO),a robust optimization approach,is employed for efficient design of a novel cascade controller for four test systems with interconnected power systems(IPSs)to tackle load-frequency con-trol(LFC)difficulties.The CGO method is based on chaos theory principles,in which the structure of fractals is seen via the chaotic game principle and the fractals’self-similarity characteristics are considered.CGO is applied in LFC studies as a novel application,which reveals further research gaps to be filled.For practical implementation,it is also highly desirable to keep the controller structure simple.Accordingly,in this paper,a CGO-based controller of fractional-order(FO)proportional-integral-derivative-FO proportional-integral(FOPID-FOPI)controller is proposed,and the integral time multiplied absolute error performance function is used.Initially,the proposed CGO-based FOPID-FOPI controller is tested with and without the nonlinearity of the governor dead band for a two-area two-source model of a non-reheat unit.This is a common test system in the literature.A two-area multi-unit system with reheater-hydro-gas in both areas is implemented.To further generalize the advantages of the proposed scheme,a model of a three-area hydrothermal IPS including generation rate constraint nonlinearity is employed.For each test system,comparisons with relevant existing studies are performed.These demonstrate the superiority of the proposed scheme in reducing settling time,and frequency and tie-line power deviations.
文摘In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.
基金National Natural Science Foundation of China(NSFC61773142,NSFC62303136)。
文摘When the maneuverability of a pursuer is not significantly higher than that of an evader,it will be difficult to intercept the evader with only one pursuer.Therefore,this article adopts a two-to-one differential game strategy,the game of kind is generally considered to be angle-optimized,which allows unlimited turns,but these practices do not take into account the effect of acceleration,which does not correspond to the actual situation,thus,based on the angle-optimized,the acceleration optimization and the acceleration upper bound constraint are added into the game for consideration.A two-to-one differential game problem is proposed in the three-dimensional space,and an improved multi-objective grey wolf optimization(IMOGWO)algorithm is proposed to solve the optimal game point of this problem.With the equations that describe the relative motions between the pursuers and the evader in the three-dimensional space,a multi-objective function with constraints is given as the performance index to design an optimal strategy for the differential game.Then the optimal game point is solved by using the IMOGWO algorithm.It is proved based on Markov chains that with the IMOGWO,the Pareto solution set is the solution of the differential game.Finally,it is verified through simulations that the pursuers can capture the escapee,and via comparative experiments,it is shown that the IMOGWO algorithm performs well in terms of running time and memory usage.
文摘The evaluation of urban flood-waterlogged vulnerability is very important to the safety of urban flood control. In this paper, the evaluation of consolidated index is used. Respectively, AHP and entropy method calculate the subjective and objective weight of the evaluation indicators, and combine them by game theory. So we can obtain synthetic weight based on objective and subjective weights. The evaluation of urban flood-waterlogged vulnerability as target layer, a single variable multi-objective fuzzy optimization model is established. We use the model to evaluate flood-waterlogged vulnerability of 13 prefecture-level city in Hunan, and compare it with other evaluation method. The results show that the evaluation method has certain adaptability and reliability, and it＇ s helpfid to the construction planning of urban flood control.
基金partly supported by the National Natural Science Foundation of China(61751303,U20A2068,11771013)the Zhejiang Provincial Natural Science Foundation of China(LD19A010001)the Fundamental Research Funds for the Central Universities。
文摘Weighted vertex cover(WVC)is one of the most important combinatorial optimization problems.In this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted networks.We first model the WVC problem as a general game on weighted networks.Under the framework of a game,we newly define several cover states to describe the WVC problem.Moreover,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the game.Then,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial time.Subsequently,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the GAA.Finally,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R281)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4331004DSR10).
文摘Computational linguistics is an engineering-based scientific discipline.It deals with understanding written and spoken language from a computational viewpoint.Further,the domain also helps construct the artefacts that are useful in processing and producing a language either in bulk or in a dialogue setting.Named Entity Recognition(NER)is a fundamental task in the data extraction process.It concentrates on identifying and labelling the atomic components from several texts grouped under different entities,such as organizations,people,places,and times.Further,the NER mechanism identifies and removes more types of entities as per the requirements.The significance of the NER mechanism has been well-established in Natural Language Processing(NLP)tasks,and various research investigations have been conducted to develop novel NER methods.The conventional ways of managing the tasks range from rule-related and hand-crafted feature-related Machine Learning(ML)techniques to Deep Learning(DL)techniques.In this aspect,the current study introduces a novel Dart Games Optimizer with Hybrid Deep Learning-Driven Computational Linguistics(DGOHDL-CL)model for NER.The presented DGOHDL-CL technique aims to determine and label the atomic components from several texts as a collection of the named entities.In the presented DGOHDL-CL technique,the word embed-ding process is executed at the initial stage with the help of the word2vec model.For the NER mechanism,the Convolutional Gated Recurrent Unit(CGRU)model is employed in this work.At last,the DGO technique is used as a hyperparameter tuning strategy for the CGRU algorithm to boost the NER’s outcomes.No earlier studies integrated the DGO mechanism with the CGRU model for NER.To exhibit the superiority of the proposed DGOHDL-CL technique,a widespread simulation analysis was executed on two datasets,CoNLL-2003 and OntoNotes 5.0.The experimental outcomes establish the promising performance of the DGOHDL-CL technique over other models.
文摘The design of controllers for robots is a complex system that is to be dealt with several tasks in real time for enabling the robots to function independently.The distributed robotic control system can be used in real time for resolving various challenges such as localization,motion controlling,mapping,route planning,etc.The distributed robotic control system can manage different kinds of heterogenous devices.Designing a distributed robotic control system is a challenging process as it needs to operate effectually under different hardware configurations and varying computational requirements.For instance,scheduling of resources(such as communication channel,computation unit,robot chassis,or sensor input)to the various system components turns out to be an essential requirement for completing the tasks on time.Therefore,resource scheduling is necessary for ensuring effective execution.In this regard,this paper introduces a novel chaotic shell game optimization algorithm(CSGOA)for resource scheduling,known as the CSGOA-RS technique for the distributed robotic control system environment.The CSGOA technique is based on the integration of the chaotic maps concept to the SGO algorithm for enhancing the overall performance.The CSGOA-RS technique is designed for allocating the resources in such a way that the transfer time is minimized and the resource utilization is increased.The CSGOA-RS technique is applicable even for the unpredicted environment where the resources are to be allotted dynamically based on the early estimations.For validating the enhanced performance of the CSGOA-RS technique,a series of simulations have been carried out and the obtained results have been examined with respect to a selected set of measures.The resultant outcomes highlighted the promising performance of the CSGOA-RS technique over the other resource scheduling techniques.
基金National Natural Science Foundation of China(Grant No.52309050,32372680)Youth Backbone Teacher Project of Henan University of Science and Technology(Grant No.13450013 and 3450010)+1 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.24B416001)Innovative Research Team(Science and Technology)in the University of Henan Province(Grant No.23IRTSTHN024).
文摘Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.
基金supported by the National Natural Science Foundation of China under Grant No.61773098the 111 Project under Grant No.B16009
文摘A switched linear quadratic（LQ） differential game over finite-horizon is investigated in this paper. The switching signal is regarded as a non-conventional player, afterwards the definition of Pareto efficiency is extended to dynamics switching situations to characterize the solutions of this multi-objective problem. Furthermore, the switched differential game is equivalently transformed into a family of parameterized single-objective optimal problems by introducing preference information and auxiliary variables. This transformation reduces the computing complexity such that the Pareto frontier of the switched LQ differential game can be constructed by dynamic programming. Finally, a numerical example is provided to illustrate the effectiveness.
