Article Review: Swarm Intelligence Optimization: An Exploration and Application of MachineLearning TechnologyToday, the Intelligence Decision Supporting System $($IDSS$)$ is being greatly employed in a variety offields, including agricultural production, transportation and environmental protection. The function ofefficient machinery and equipment in the development and growth of agriculture is critical. Theauthors claim that the majority of applications researched for machine learning technology andmachine learning algorithms are based on swarm intelligence optimization. The authors usedV$3$CFOA to create an optimal V$3$CFOA$-$RF model. The authors prove that the proposed algorithmwas also useful in the development of intelligent agriculture instruments, which resulted in an increasein productivity.The model flow: During initial stage, the size of the population is given, afte that the rice cropprojection is made. As an initialization, the algorithm has been given the iteration for training and seedpoints. In the initialization process, the chaotic sequence was also provided. The optimizationparameters for the rice prediction were determined in the next two steps, and the output fitness wascomputed for every outcome. The coordinate position of the fly has been recorded, and its location isbeen constantly changed. The best values are given based on the end output of the parameters, onceall the iterations have been completed.Comparison of the algorithm proposed and other conventional ones: The execution of theprototype model, is a measure which, if satisfied, is demonstrated by comparison and analysis of thealgorithm posited in the article with various traditional algorithms. The author chose FOA$-$RF$,$ PSO$-$RF$,$and SVM as contrast objects based on a study of common prediction algorithm models, andcompared the results of each method to the results of the V$3$CFOA$-$RF algorithm to determine theusefulness of the V$3$CFOARF algorithm. The authors utilized the proposed pseudo code to find $2$features in RF$,$ get the optimal parameter value, and then predict the count of pests to get satisfactoryprediction results.Observation through Data analysis: It was observed that the proposed approach has intersected,whereas FOA$-$RF has a greater RMSE value; however, not until it reached the $200$th step the FOA$-$RFbegins to intersect, whereas V$3$CFOA reaches near to the required solution and the FOA$-$RF has toachieve the better outcome, despite utilising higher amount of iterations. The authors observed that interms of all three error measures like RMSE, MAE, and the correlation coefficient R$,$ the V$3$CFOA$-$RFoutperforms PSO$-$RF$,$ FOA$-$RF$,$ and SVM$.$Conclusion: The findings by the authors reveal that suggested approach is optimal in recognition andprediction, and it is able to partially solve the time delay problem. The approach assisted in improvingprecision in prediction, resulting in efficient rice outcome. As a result, the model can be used in takingkeen decisions in the crop production.Reference Information:Cai, Yinying and Sharma, Amit. "Swarm Intelligence Optimization: An Exploration and Application ofMachine Learning Technology" Journal of Intelligent Systems, vol. $30,$ no$.1,2021,$ pp$.460-469.$https:$//$doi$.$org$/10\mathrm{.}1515/$jisys$-2020-0084$Please read the paper provided in this module and give your critique on it$.$ No more than one page... $.$ Make sure to include these three items:

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