Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.

Research on stock trend prediction method based on optimized random forest

As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

Risk assessment of rockburst using SMOTE oversampling and integration algorithms under GBDT framework

Rockburst is a common geological disaster in underground engineering,which seriously threatens the safety of personnel,equipment and property.Utilizing machine learning models to evaluate risk of rockburst is gradually becoming a trend.In this study,the integrated algorithms under Gradient Boosting Decision Tree(GBDT)framework were used to evaluate and classify rockburst intensity.First,a total of 301 rock burst data samples were obtained from a case database,and the data were preprocessed using synthetic minority over-sampling technique(SMOTE).Then,the rockburst evaluation models including GBDT,eXtreme Gradient Boosting(XGBoost),Light Gradient Boosting Machine(LightGBM),and Categorical Features Gradient Boosting(CatBoost)were established,and the optimal hyperparameters of the models were obtained through random search grid and five-fold cross-validation.Afterwards,use the optimal hyperparameter configuration to fit the evaluation models,and analyze these models using test set.In order to evaluate the performance,metrics including accuracy,precision,recall,and F1-score were selected to analyze and compare with other machine learning models.Finally,the trained models were used to conduct rock burst risk assessment on rock samples from a mine in Shanxi Province,China,and providing theoretical guidance for the mine's safe production work.The models under the GBDT framework perform well in the evaluation of rockburst levels,and the proposed methods can provide a reliable reference for rockburst risk level analysis and safety management.

A path planning method for robot patrol inspection in chemical industrial parks

Safety patrol inspection in chemical industrial parks is a complex multi-objective task with multiple degrees of freedom.Traditional pointer instruments with advantages like high reliability and strong adaptability to harsh environment,are widely applied in such parks.However,they rely on manual readings which have problems like heavy patrol workload,high labor cost,high false positives/negatives and poor timeliness.To address the above problems,this study proposes a path planning method for robot patrol in chemical industrial parks,where a path optimization model based on improved iterated local search and random variable neighborhood descent(ILS-RVND)algorithm is established by integrating the actual requirements of patrol tasks in chemical industrial parks.Further,the effectiveness of the model and algorithm is verified by taking real park data as an example.The results show that compared with GA and ILS-RVND,the improved algorithm reduces quantification cost by about 24%and saves patrol time by about 36%.Apart from shortening the patrol time of robots,optimizing their patrol path and reducing their maintenance loss,the proposed algorithm also avoids the untimely patrol of robots and enhances the safety factor of equipment.

Random walk search in unstructured P2P

Unstructured P2P has power-law link distribution, and the random walk in power-law networks is analyzed. The analysis results show that the probability that a random walker walks through the high degree nodes is high in the power-law network, and the information on the high degree nodes can be easily found through random walk. Random walk spread and random walk search method （RWSS） is proposed based on the analysis result. Simulation results show that RWSS achieves high success rates at low cost and is robust to high degree node failure.

An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps forWSN

Wireless Sensor Networks(WSNs)have hardware and software limitations and are deployed in hostile environments.The problem of energy consumption in WSNs has become a very important axis of research.To obtain good performance in terms of the network lifetime,several routing protocols have been proposed in the literature.Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency.It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent,and then the parent node forwards them,directly or via other parent nodes,to the base station(sink).In this paper,we present a new Energy-Efficient clustering protocol for WSNs using an Objective Function and Random Search with Jumps(EEOFRSJ)in order to reduce sensor energy consumption.First,the objective function is used to find an optimal cluster formation taking into account the ratio of the mean Euclidean distance of the nodes to their associated cluster heads(CH)and their residual energy.Then,we find the best path to transmit data from the CHs nodes to the base station(BS)using a random search with jumps.We simulated our proposed approach compared with the Energy-Efficient in WSNs using Fuzzy C-Means clustering(EEFCM)protocol using Matlab Simulink.Simulation results have shown that our proposed protocol excels regarding energy consumption,resulting in network lifetime extension.

AN ANALYSIS ABOUT BEHAVIOR OF EVOLUTIONARY ALGORITHMS:A KIND OF THEORETICAL DESCRIPTION BASED ON GLOBAL RANDOM SEARCH METHODS

Evolutionary computation is a kind of adaptive non--numerical computation method which is designed tosimulate evolution of nature. In this paper, evolutionary algorithm behavior is described in terms of theconstruction and evolution of the sampling distributions over the space of candidate solutions. Iterativeconstruction of the sampling distributions is based on the idea of the global random search of generationalmethods. Under this frame, propontional selection is characterized as a gobal search operator, and recombination is characerized as the search process that exploits similarities. It is shown-that by properly constraining the search breadth of recombination operators, weak convergence of evolutionary algorithms to aglobal optimum can be ensured.

Improvement of Pure Random Search in Global Optimization

In this paper, the improvement of pure random search is studied. By taking some information of the function to be minimized into consideration, the authors propose two stochastic global optimization algorithms. Some numerical experiments for the new stochastic global optimization algorithms are presented for a class of test problems.

Curupira V1.0: Joint Inversion of VES and TEM for Environmental and Mass Movements Studies

An innovative inversion code, named “Curupira v1.0”, has been developed using Matlab to determine the vertical distribution of resistivity beneath the subsoil. The program integrates Vertical Electrical Sounding (VES), successful in shallow subsurface exploration and Time Domain Electromagnetic (TEM) techniques, better suited for deeper exploration, both of which are widely employed in geophysical exploration. These methodologies involve calculating subsurface resistivity through appropriate inversion processes. To address the ill-posed nature of inverse problems in geophysics, a joint inversion scheme combining VES and TEM data has been incorporated into Curupira v1.0. The software has been tested on both synthetic and real-world data, the latter of which was acquired from the Parana sedimentary basin which we summarise here. The results indicate that the joint inversion of VES and TEM techniques offers improved recovery of simulated models and demonstrates significant potential for hydrogeological studies.

A Modified Algorithm for Nonliear Integer Programming

Based on the analysis to the random sear ch algorithm of LUUS, a modified random directed integer search algorithm (MRDI SA) is given for first time. And a practical example is given to show that the adva ntage of this kind of algorithm is the reliability can’t be infuenced by the ini tial value X (0) and the start search domain R (0) . Besides, i t can be applied to solve the higher dimensional constrained nonlinear integer p rogramming problem.

Critical Slope and Plasticity

Limit analysis based on upper bound theorem into slope stability is presented. A rotational failure mechanism (log spiral) passing through the toe in an inclined slope is assumed for getting the critical height. The proposed limit analysis, although on the kinematical admissible velocity field, always satisfies the equilibrium of forces acting on sliced rigid blocks. And the most critical slip surface can be searched by random technique. A new solution scheme is also developed for rapid searching critical slip surface. It is also applicable to a variety of slope models. The method is shown having a high accuracy compared with limit solution for simple slope.

Stochastic focusing search:a novel optimization algorithm for real-parameter optimization

A novel optimization algorithm called stochastic focusing search （SFS） for the real-parameter optimization is proposed. The new algorithm is a swarm intelligence algorithm, which is based on simulating the act of human randomized searching, and the human searching behaviors. The algorithm＇s performance is studied using a challenging set of typically complex functions with comparison of differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms, and the simulation results show that SFS is competitive to solve most parts of the benchmark problems and will become a promising candidate of search algorithms especially when the existing algorithms have some difficulties in solving certain problems.

ANew Theoretical Framework forAnalyzing Stochastic Global Optimization Algorithms

In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.

DESIGN OF MAXIMALLY FLAT FIR FILTERS BASED ON EXPLICIT FORMULAS COMBINED WITH OPTIMIZATION

A maximally flat FIR filter design method based on explicit formulas combined with simulated annealing and random search was presented. Utilizing the explicit formulas to calculate the ini- tial values, the firate-word-length FIR filter design problem was converted into optimization of the filter coefficients, An optimization method combined with local discrete random search and simulated annealing was proposed, with the result of optimum solution in the sense of Chebyshev approximation. The proposed method can simplify the design process of FIR filter and reduce the calculation burden. The simulation result indicates that the proposed method is superior to the traditional round off method and can reduce the value of the objective function to 41%～74%.

Method to deploy wireless relays in industrial wireless monitoring

In light of demands for wireless monitoring and the characteristics of wireless channel,a complete deployment method containing channel survey,path loss estimation,and gradient grade of wireless relay nodes is proposed.It can be proved by experiments that under the premise of meeting the requirements of real-time and redundant-topology,the total number of relay nodes could be minimized by using the proposed method.

A GRASP Algorithm for Multi-objective Circuit Partitioning

Circuit partitioning plays a crucial role in very large-scale integrated circuit (VLSI) physical design automation. With current trends, partitioning with multiple objectives which includes cutsize, area, delay, and power obtains much concentration. In this paper, a multi-objective greedy randomized adaptive search procedure (GRASP) is presented for simultaneous cutsize and circuit delay minimization. Each objective is assigned a preference or weight to direct the search procedure and generate a variety of efficient solutions by changing the preference. To get a good initial partition with minimal cutsize and circuit delay, the gain of each module in a circuit is computed by considering both signal nets and circuit delay. The performance of the proposed algorithm is evaluated on a standard set of partitioning benchmark. The experimental results show that the proposed algorithm can generate a set of Pareto optimal solutions and is efficient for tackling multi-objective circuit partitioning.

Random Search and Code Similarity-Based Automatic Program Repair

In recent years,automatic program repair approaches have developed rapidly in the field of software engineering.However,the existing program repair techniques based on genetic programming suffer from requiring verification of a large number of candidate patches,which consume a lot of computational resources.In this paper,we propose a random search and code similarity based automatic program repair(RSCSRepair).First,to reduce the verification computation effort for candidate patches,we introduce test filtering to reduce the number of test cases and use test case prioritization techniques to reconstruct a new set of test cases.Second,we use a combination of code similarity and random search for patch generation.Finally,we use a patch overfitting detection method to improve the quality of patches.In order to verify the performance of our approach,we conducted the experiments on the Defects4J benchmark.The experimental results show that RSCSRepair correctly repairs up to 54 bugs,with improvements of 14.3%,8.5%,14.3%and 10.3%for our approach compared with jKali,Nopol,CapGen and Sim Fix,respectively.

Distribution System Optimization Planning Based on Plant Growth Simulation Algorithm

An approach for the integrated optimization of the construction/expansion capacity of high-voltage/ medium-voltage (HV/MV) substations and the configuration of MV radial distribution network was presented using plant growth simulation algorithm (PGSA). In the optimization process, fixed costs correspondent to the investment in lines and substations and the variable costs associated to the operation of the system were considered under the constraints of branch capacity, substation capacity and bus voltage. The optimization variables considerably reduce the dimension of variables and speed up the process of optimizing. The effectiveness of the proposed approach was tested by a distribution system planning.

Predicting non-carcinogenic hazard quotients of heavy metals in pepper (Capsicum annum L.) utilizing electromagnetic waves

Given the tendency of heavy metals to accumulate in soil and plants,the purpose of this study was to determine the contamination.levels of Cd,Ni,Pb,and Zn on peppers(leaves.and fruit),grown in contaminated soils in industral centers.For this purpose,we measured the uptake of the four heavy metals by peppers grown in the heavy metal contaminated soils throughout the four growth stages:two-leaf,growth,fiowering,and fruiting;and calculated various vegetation indices to evaluate the heavy metal contamination potentials.Electromagnetic waves were also applied for analyzing the responses of the target plants to various heavy metals.Based on the relevant spectral bands identified by principal component analysis(PCA)and random search methods,a regression method was then employed.to determine the most optimal.spectral bands for estimating,the target hazard quotient(THQ).The THQ was found to be the highest in the plants contaminated by Pb(THQ=62)and Zn(THQ=5.07).The results of PCA and random search indicated that the spectra at the bands of b570,b650,and b760 for Pb,b400 and b880 for Ni,b560 and b880 for Cd,and b560 b910,and b1050 for Zn were the most optimal spectra for assessing THQ.Therefore,in future studies,instead of examining the amount of heavy metals in plants by chemical analysis in the laboratory,the responses of the plants to the electromagnetic waves in the identified bands can be redily investigated in the field based on the established correlations.