Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the patte...Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.展开更多
We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,th...We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.展开更多
The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor net...The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.展开更多
In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ...In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.展开更多
Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resu...Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.展开更多
为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并...为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并且利用该算法自动选取径向基函数RBF(radial basis function)神经网络中的输入变量,以及对网络中隐含层到输出层之间的权值进行训练,从而减少了RBF神经网络的工作量,提高了训练速度。用优化后的RBF神经网络进行短期负荷预测,结果表明,该方法具有较高的预测精度。展开更多
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 2/142/43).
文摘Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.
基金Project(51779052)supported by the National Natural Science Foundation of ChinaProject(QC2016062)supported by the Natural Science Foundation of Heilongjiang Province,China+2 种基金Project(614221503091701)supported by the Research Fund from Science and Technology on Underwater Vehicle Laboratory,ChinaProject(LBH-Q17046)supported by the Heilongjiang Postdoctoral Funds for Scientific Research Initiation,ChinaProject(HEUCFP201741)supported by the Fundamental Research Funds for the Central Universities,China
文摘We introduce the artificial fish swarm algorithm for heading motion model identification and control parameter optimization problems for the“Ocean Rambler”unmanned wave glider(UWG).First,under certain assumptions,the rigid-flexible multi-body system of the UWG was simplified as a rigid system composed of“thruster+float body”,based on which a planar motion model of the UWG was established.Second,we obtained the model parameters using an empirical method combined with parameter identification,which means that some parameters were estimated by the empirical method.In view of the specificity and importance of the heading control,heading model parameters were identified through the artificial fish swarm algorithm based on tank test data,so that we could take full advantage of the limited trial data to factually describe the dynamic characteristics of the system.Based on the established heading motion model,parameters of the heading S-surface controller were optimized using the artificial fish swarm algorithm.Heading motion comparison and maritime control experiments of the“Ocean Rambler”UWG were completed.Tank test results show high precision of heading motion prediction including heading angle and yawing angular velocity.The UWG shows good control performance in tank tests and sea trials.The efficiency of the proposed method is verified.
基金financially supported by Natural Science Foundation of Heilongjiang Province of China[Grant No.LH2019F042].
文摘The main objective of the present study is the development of a new algorithm that can adapt to complex and changeable environments.An artificial fish swarm algorithm is developed which relies on a wireless sensor network(WSN)in a hydrodynamic background.The nodes of this algorithm are viscous fluids and artificial fish,while related‘events’are directly connected to the food available in the related virtual environment.The results show that the total processing time of the data by the source node is 6.661 ms,of which the processing time of crosstalk data is 3.789 ms,accounting for 56.89%.The total processing time of the data by the relay node is 15.492 ms,of which the system scheduling and the Carrier Sense Multiple Access(CSMA)rollback time of the forwarding is 8.922 ms,accounting for 57.59%.The total time for the data processing of the receiving node is 11.835 ms,of which the processing time of crosstalk data is 3.791 ms,accounting for 32.02%;the serial data processing time is 4.542 ms,accounting for 38.36%.Crosstalk packets occupy a certain amount of system overhead in the internal communication of nodes,which is one of the causes of node-level congestion.We show that optimizing the crosstalk phenomenon can alleviate the internal congestion of nodes to some extent.
基金supported by the National Natural Science Foundation of China(61472441)
文摘In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
文摘Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.
文摘为了提高人工鱼群算法AFSA(artificial fish swarm algorithm)的全局搜索能力及加快其收敛速度,提出一种将其与免疫算法IA(immune algorithm)进行结合的新方法,形成了免疫人工鱼群算法IAFSA(immuneartificial fish swarm algorithm),并且利用该算法自动选取径向基函数RBF(radial basis function)神经网络中的输入变量,以及对网络中隐含层到输出层之间的权值进行训练,从而减少了RBF神经网络的工作量,提高了训练速度。用优化后的RBF神经网络进行短期负荷预测,结果表明,该方法具有较高的预测精度。
