Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

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.

Interaction model of artificial fish in virtual environment

Conventional artificial fish has some shortages on the interaction with environment, other fish, and the animator. This article proposes a multi-tier interaction control model of artificial fish, realizes the interaction model through integration of virtual reality technology and Markov sequence, and provides a virtual marine world to describe the interaction between artificial fish and the virtual environment and the interaction between the artificial fish and the animator. Simulation results show that the interaction model owns not only the basic characteristics of virtual biology, but also has high trueness interaction function.

An Evaluation of Artificial Fish Nest for Assessment Enhancement Effects of Fishery Resources of in Xiangjiang River

Artificial fish nests are set in Lukou section and Hengyang section of Xiangjiang River from March 20 to May 10, 2019. The structure of artificial fish nest is bamboo frame, with the size of 2.5 m × 5.0 m × 4 and a unit frame area of 60 m2. A total of 58,201 eggs were collected by 24 monitoring times, of which 30,441 were from Lukou and 27,760 from Hengyang. 3831 fish eggs identify 8 fish species using morphological or molecular biological methods. The results showed that the number of eggs peaked from the 8th day to the 24th day after the nest entered the water, and then decreased rapidly. There was significant negative correlation between egg number in per m2 fish nest and days of fish nest (P < 0.01). The economic benefits of artificial fish nests were RMB 11.81 million. The artificial fish nest can not only increase the population of fish, but also has significant economic benefits.

Artificial Fish Swarm for Multi Protein Sequences Alignment in Bioinformatics

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.

Development of an Artificial Fish Swarm Algorithm Based on aWireless Sensor Networks in a Hydrodynamic Background

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.

Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis

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.

A kind of fast Gaussian particle filter based on Artificial Fish School Algorithm

This paper proposes an improved Gaussian particle filter integratingthe Artificial Fish School Algorithm to optimise the measured values to improve the overall estimation accuracy of the system.Meanwhile,it also solves the problems of susceptibility to interference and insufficient estimation accuracy in nonlinear systems.Furthermore,since the calculation time of the fusion algorithm increases,in order to ensure the speed of state estimation,the linear transformation of standard particle swarm is used to replace the particle sampling link of Gaussian particle filter.Simulation results show that the calculation speed of a fast Gaussian Particle Filter based on the Artificial Fish School Algorithm is 21.7%faster than the Particle Filter based on the Artificial Fish School Algorithm.Compared with Particle Filter,Gaussian particle filter,and the Artificial Fish School Algorithm,the proposed algorithm has a higher accuracy.

Approach to WTA in air combat using IAFSA-IHS algorithm

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.

A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems:Applications and Trends

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.

А Novel AP Placement Algorithm Based on User Distribution for Indoor WLAN System

AP deployment is significant for indoor WLAN system to achieve seamless coverage. The available algorithms do not take user distribution into consideration so that poor user coverage and imbalanced network load occur. Therefore, this paper proposed a novel AP placement algorithm to bridge the AP deployment with user distribution. The proposed algorithm employs statistics theory to model the user distribution as its location and probability. Then we obtain the AP location based on the fuzzy C-clustering algorithm. The proposed algorithm is practical for implementation, which means the actual signal transmission isn't required in our proposed method. The simulation results show that the proposed algorithm could automatically achieve a good AP deployment with different user distribution, and provide a good performance in the maximum users and AP load balance in WLAN.

Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

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.

Performance evaluation model of cross border e-commerce supply chain based on LMBP feedback neural network

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.

Optimized Selection Method of Air Combat Course of Action under Stochastic Uncertainty

Aiming at the design problem of aviation swarm combat course of action(COA),considering the influence of stochastic parameters in the causal relationship model and optimization problem model,according to the dynamic influence net(DIN)theory,stochastic simulation technique,feedforward neural network(FNN)function approximation technique and multi-objective artificial fish school algorithm(MOAFSA),this paper proposed a COA optimized method based on DIN and multi-objective stochastic chance constraint optimization for aviation swarm combat.First,on the basis of establishing the overall framework of the model and defining the elements of causal relationship modeling,the static and dynamic causal relationship modeling and optimization problem modeling were carried out respectively.Second,the probability propagation mechanism of DIN was established,which mainly included two aspects,i.e.,the overall process and the specific algorithm.Then,input and output data were generated based on stochastic simulation.According to these data,FNN was adopted for function approximation,and MOAFSA was adopted for iterative optimization.Finally,the rationality of the model,and the effectiveness and superiority of the algorithm were verified through multiple sets of simulation cases.