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Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach 被引量:1
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作者 Saud S.Alotaibi Eatedal Alabdulkreem +5 位作者 Sami Althahabi Manar Ahmed Hamza Mohammed Rizwanullah Abu Sarwar Zamani Abdelwahed Motwakel Radwa Marzouk 《Computer Systems Science & Engineering》 SCIE EI 2023年第4期737-751,共15页
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
关键词 Sentiment analysis opinion mining natural language processing artificial fish swarm algorithm deep learning
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Unmanned wave glider heading model identification and control by artificial fish swarm algorithm 被引量:2
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作者 WANG Lei-feng LIAO Yu-lei +2 位作者 LI Ye ZHANG Wei-xin PAN Kai-wen 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第9期2131-2142,共12页
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
关键词 unmanned wave glider artificial fish swarm algorithm heading model parameters identification control parameters optimization
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Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics
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作者 Medhat A.Tawfeek Saad Alanazi A.A.Abd El-Aziz 《Computers, Materials & Continua》 SCIE EI 2022年第9期6091-6106,共16页
The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very complex.There is a trade-off in the objectives in the existing techniques of Multiple... The alignment operation between many protein sequences or DNAsequences related to the scientific bioinformatics application is very complex.There is a trade-off in the objectives in the existing techniques of MultipleSequence Alignment (MSA). The techniques that concern with speed ignoreaccuracy, whereas techniques that concern with accuracy ignore speed. Theterm alignment means to get the similarity in different sequences with highaccuracy. The more growing number of sequences leads to a very complexand complicated problem. Because of the emergence;rapid development;anddependence on gene sequencing, sequence alignment has become importantin every biological relationship analysis process. Calculating the numberof similar amino acids is the primary method for proving that there is arelationship between two sequences. The time is a main issue in any alignmenttechnique. In this paper, a more effective MSA method for handling themassive multiple protein sequences alignment maintaining the highest accuracy with less time consumption is proposed. The proposed method dependson Artificial Fish Swarm (AFS) algorithm that can break down the mostchallenges of MSA problems. The AFS is exploited to obtain high accuracyin adequate time. ASF has been increasing popularly in various applicationssuch as artificial intelligence, computer vision, machine learning, and dataintensive application. It basically mimics the behavior of fish trying to getthe food in nature. The proposed mechanisms of AFS that is like preying,swarming, following, moving, and leaping help in increasing the accuracy andconcerning the speed by decreasing execution time. The sense organs that aidthe artificial fishes to collect information and vision from the environmenthelp in concerning the accuracy. These features of the proposed AFS make thealignment operation more efficient and are suitable especially for large-scaledata. The implementation and experimental results put the proposed AFS as afirst choice in the queue of alignment compared to the well-known algorithmsin multiple sequence alignment. 展开更多
关键词 Multiple sequence alignment swarm intelligence artificial fish swarm protein sequences
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Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background
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作者 Sheng Bai Feng Bao +1 位作者 Fengzhi Zhao Miaomiao Liu 《Fluid Dynamics & Materials Processing》 EI 2020年第5期935-946,共12页
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
关键词 artificial fish swarm algorithm wireless sensor network network measurement HYDRODYNAMICS
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Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis 被引量:1
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作者 Syed Haroon Abdul Gafoor Padma Theagarajan 《International Journal of Intelligent Computing and Cybernetics》 EI 2022年第4期540-561,共22页
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
关键词 Parkinson disease dysphonia features Feature subset selection Score-based artificial fish swarm algorithm(SAFSA) Singular value decomposition(SVD) Classification
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A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends 被引量:39
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作者 Jun Tang Gang Liu Qingtao Pan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第10期1627-1643,共17页
Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In th... Swarm intelligence algorithms are a subset of the artificial intelligence(AI)field,which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications.In the past decades,numerous swarm intelligence algorithms have been developed,including ant colony optimization(ACO),particle swarm optimization(PSO),artificial fish swarm(AFS),bacterial foraging optimization(BFO),and artificial bee colony(ABC).This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures.It provides an overview of the various swarm intelligence algorithms and their advanced developments,and briefly provides the description of their successful applications in optimization problems of engineering fields.Finally,opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments. 展开更多
关键词 Ant colony optimization(ACO) artificial bee colony(ABC) artificial fish swarm(AFS) bacterial foraging optimization(BFO) optimization particle swarm optimization(PSO) swarm intelligence
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Approach to WTA in air combat using IAFSA-IHS algorithm 被引量:11
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作者 LI Zhanwu CHANG Yizhe +3 位作者 KOU Yingxin YANG Haiyan XU An LI You 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2018年第3期519-529,共11页
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
关键词 air combat weapon target assignment improved artificial fish swarm algorithm-improved harmony search algorithm(IAFSA-IHS) artificial fish swarm algorithm(AFSA) harmony search(HS)
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Dynamic friction modelling and parameter identification for electromagnetic valve actuator 被引量:6
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作者 SHAO Da XU Si-chuan DU Ai-min 《Journal of Central South University》 SCIE EI CAS CSCD 2018年第12期3004-3020,共17页
A new modified LuGre friction model is presented for electromagnetic valve actuator system.The modification to the traditional LuGre friction model is made by adding an acceleration-dependent part and a nonlinear cont... A new modified LuGre friction model is presented for electromagnetic valve actuator system.The modification to the traditional LuGre friction model is made by adding an acceleration-dependent part and a nonlinear continuous switch function.The proposed new friction model solves the implementation problems with the traditional LuGre model at high speeds.An improved artificial fish swarm algorithm(IAFSA)method which combines the chaotic search and Gauss mutation operator into traditional artificial fish swarm algorithm is used to identify the parameters in the proposed modified LuGre friction model.The steady state response experiments and dynamic friction experiments are implemented to validate the effectiveness of IAFSA algorithm.The comparisons between the measured dynamic friction forces and the ones simulated with the established mathematic friction model at different frequencies and magnitudes demonstrate that the proposed modified LuGre friction model can give accurate simulation about the dynamic friction characteristics existing in the electromagnetic valve actuator system.The presented modelling and parameter identification methods are applicable for many other high-speed mechanical systems with friction. 展开更多
关键词 LuGre friction model artificial fish swarm algorithm Gauss mutation chaotic search parameter identification electromagnetic valve actuator
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Stroke optimization of a novel multi-station rotary polishing robot based on workspace analysis 被引量:1
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作者 李东京 Wei Wang +2 位作者 Wang Qilong Hao Daxian Jin Hui 《High Technology Letters》 EI CAS 2018年第3期313-321,共9页
In order to meet the polishing requirement of faucets and other products,a novel multi-station rotary polishing robot is designed,which is a PPPR + RR type of degree of freedom( DOF) distribution structure,and is simi... In order to meet the polishing requirement of faucets and other products,a novel multi-station rotary polishing robot is designed,which is a PPPR + RR type of degree of freedom( DOF) distribution structure,and is similar to dual-arm robot. Forward and inverse kinematic analysis is carried out by robot modeling. In order to make this robot structure more compact,first of all,X,Y and Z three moving degrees of freedom( DOF) limit stroke polishing need is calculated by using an artificial fish swarm algorithm,which analyzes dexterous workspace of this robot. Then,on the basis of the above analysis,the three DOF stroke is optimized. Simulation and polishing experimental results verify that this polishing robot with optimized stroke parameters can meet the polishing needs of faucets and other bathroom pieces. 展开更多
关键词 multi-station rotary table polishing robot dexterous workspace analysis stroke optimization dual-arm robot artificial fish swarm algorithm (AFSA)
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Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model
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作者 Basim Aljabhan Mahmoud Ragab +1 位作者 Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi 《Computers, Materials & Continua》 SCIE EI 2022年第12期5269-5282,共14页
Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missi... Traffic flow prediction becomes an essential process for intelligent transportation systems(ITS).Though traffic sensor devices are manually controllable,traffic flow data with distinct length,uneven sampling,and missing data finds challenging for effective exploitation.The traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical models.The recent developments of statistic and deep learning(DL)models pave a way for the effectual design of traffic flow prediction(TFP)models.In this view,this study designs optimal attentionbased deep learning with statistical analysis for TFP(OADLSA-TFP)model.The presentedOADLSA-TFP model intends to effectually forecast the level of traffic in the environment.To attain this,the OADLSA-TFP model employs attention-based bidirectional long short-term memory(ABLSTM)model for predicting traffic flow.In order to enhance the performance of the ABLSTM model,the hyperparameter optimization process is performed using artificial fish swarm algorithm(AFSA).A wide-ranging experimental analysis is carried out on benchmark dataset and the obtained values reported the enhancements of the OADLSA-TFP model over the recent approaches mean square error(MSE),root mean square error(RMSE),and mean absolute percentage error(MAPE)of 120.342%,10.970%,and 8.146%respectively. 展开更多
关键词 Traffic flow prediction deep learning artificial fish swarm algorithm mass gatherings statistical analysis LOGISTICS
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Application of Light Reflectance-Transmittance Measurement Method to Reconstruct Geometrical Morphology of Particle Fractal Aggregates
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作者 LIU Zhigang FANG Hongyi +2 位作者 ZHU Ruihan HE Zhenzong MAO Junkui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2021年第1期57-67,共11页
Particles,including soot,aerosol and ash,usually exist as fractal aggregates.The radiative properties of the particle fractal aggregates have a great influence on studying the light or heat radiative transfer in the p... Particles,including soot,aerosol and ash,usually exist as fractal aggregates.The radiative properties of the particle fractal aggregates have a great influence on studying the light or heat radiative transfer in the particle medium.In the present work,the performance of the single-layer inversion model and the double-layer inversion model in reconstructing the geometric structure of particle fractal aggregates is studied based on the light reflectancetransmittance measurement method.An improved artificial fish-swarm algorithm(IAFSA)is proposed to solve the inverse problem.The result reveals that the accuracy of double-layer inversion model is more satisfactory as it can provide more uncorrelated information than the single-layer inversion model.Moreover,the developed IAFSA show higher accuracy and better robustness than the original artificial fish swarm algorithm(AFSA)for avoiding local optimization problems effectively.As a whole,the present work supplies a useful kind of measurement technology for predicting geometrical morphology of particle fractal aggregates. 展开更多
关键词 inversion radiative problem artificial fish swarm algorithm radiative property particle fractal aggregate geometrical morphology
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SVC Video Transmission Optimization Algorithm in Software Defined Network
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作者 Zhe Liu 《China Communications》 SCIE CSCD 2018年第10期143-149,共7页
Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Softwar... Scalable video coding(SVC) is a powerful tool to solve the network heterogeneity and terminal diversity in video applications. However, in related works about the optimization of SVC-based video streaming over Software Defined Network(SDN), most of the them are focused either on the number of transmission layers or on the optimization of transmission path for specific layer. In this paper, we propose a noval optimization algorithm for SVC to dynamically adjust the number of layers and optimize the transmission paths simultaneously. We establish the problem model based on the 0/1 knapsack model, and then solve it with Artificial Fish Swarm Algorithm. Additionally, the simulations are carried out on the Mininet platform, which show that our approach can dynamically adjust the number of layers and select the optimal paths at the same time. As a result, it can achieve an effective allocation of network resources which mitigates the congestion and reduces the loss of non-SVC stream. 展开更多
关键词 SVC SDN OpenFlow Mininet artificial fish swarm Algorithm (AFSA) 0/1 knapsack model
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Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network
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作者 Ling Tan 《Intelligent and Converged Networks》 EI 2023年第2期168-180,共13页
In recent years,with the support of national policies,Cross Border E-Commerce(CBEC)has developed rapidly.This business model not only brings significant benefits to the national economy,but also has many unique challe... In recent years,with the support of national policies,Cross Border E-Commerce(CBEC)has developed rapidly.This business model not only brings significant benefits to the national economy,but also has many unique challenges,especially at the level of supply chain management.Therefore,to enable CBEC enterprises to develop sustainable supply chain,this study discusses the performance evaluation model of supply chain and proposes a CBEC Supply Chain Performance Evaluation Model(CBECSC-EM)based on the Levenberg–Marquardt Backpropagation(LMBP)algorithm.This experiment constructs performance evaluation indicators for the supply chain of CBEC enterprises.On this basis,the LMBP algorithm is introduced,and improved in the experiment to make the overall performance of the evaluation model more scientific and reasonable.In the verification set,the maximum F1 values of LMBP,DEA,SBM,and BP are 98.46%,93.78%,87.29%,and 78.95%,respectively.The MAPE value of LMBP model is 0.102%,which is lower than the other three methods(0.282%,0.343%,and 0.385%)selected in the experiment.The maximum standard deviation rates of importance and operability of the evaluation indexes are 0.1346 and 0.1405,respectively,and there is a significant consistency between the expert scores.Therefore,the LMBP algorithm has broad application prospects in supply chain performance evaluation of CBEC enterprises. 展开更多
关键词 Levenberg–Marquardt Backpropagation(LMBP)algorithm Cross Border E-Commerce(CBEC) supply chain performance evaluation evaluation indicators artificial fish swarm algorithm
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