Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term...Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.展开更多
During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and...During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.展开更多
In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed me...In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.展开更多
Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically ...Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.展开更多
Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a da...Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining.Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimizing the FS problem.The White Shark Optimizer(WSO),as a metaheuristic algorithm,primarily simulates the behavior of great white sharks’sense of hearing and smelling during swimming and hunting.However,it fails to consider their other randomly occurring behaviors,for example,Tail Slapping and Clustered Together behaviors.The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm.The Clustered Together behavior includes access to food and mating,which can change the direction of local search and enhance local utilization.It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer(IWSO).The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions,and the test results of IWSO are compared with other metaheuristic algorithms,which proves that IWSO combining the two behaviors has a stronger search capability.Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model,which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor(KNN)on 16 benchmark datasets and the results are compared with 7 metaheuristics.Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.展开更多
In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used t...In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.展开更多
The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effectiv...The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
The Bugaji area,situated within the Malumfashi Schist Belt of northwestern Nigeria,primarily consists of metasediments that include quartzo-feldspathic and pelitic schists,and gneiss.However,this area poses a challeng...The Bugaji area,situated within the Malumfashi Schist Belt of northwestern Nigeria,primarily consists of metasediments that include quartzo-feldspathic and pelitic schists,and gneiss.However,this area poses a challenge in mineral exploration due to limited outcrop exposures and complex subsurface structures.Hence,there is the need for exhaustive geophysical studies and supplementary approaches to accurately delineate lithologies and structures.Therefore,this study combines field mapping and geophysical techniques with artificial intelligence(AI)modeling,comprising supervised learning algorithms,to overcome this exploration problem.Utilizing sophisticated AI techniques,specifically the Random Forest Classifier and K-Nearest Neighbor algorithms,geophysical data(gravity,magnetic,and radiometric measurements)were processed and analyzed.The AI model effectively filled data gaps,and identified potential lithological variations and prospective mineralization zones based on geophysical signatures derived from the integrated dataset.The AI modeling's commendable average accuracy of 85%in predicting values underscores its efficacy in interpreting geophysical data.The success of random forest in the geological mapping process can be attributed to its ability to handle high-dimensional data,capture non-linear relationships between input variables,and mitigate overfitting.The integrated approach enhanced our understanding of subsurface geology in the Bugaji area.展开更多
The complexity and unpredictability of clear air turbulence(CAT)pose significant challenges to aviation safety.Accurate prediction of turbulence events is crucial for reducing flight accidents and economic losses.Howe...The complexity and unpredictability of clear air turbulence(CAT)pose significant challenges to aviation safety.Accurate prediction of turbulence events is crucial for reducing flight accidents and economic losses.However,traditional turbulence prediction methods,such as ensemble forecasting techniques,have certain limitations:they only consider turbulence data from the most recent period,making it difficult to capture the nonlinear relationships present in turbulence.This study proposes a turbulence forecasting model based on the K-nearest neighbor(KNN)algorithm,which uses a combination of eight CAT diagnostic features as the feature vector and introduces CAT diagnostic feature weights to improve prediction accuracy.The model calculates the results of seven years of CAT diagnostics from 125 to 500 hPa obtained from the ECMWF fifth-generation reanalysis dataset(ERA5)as feature vector inputs and combines them with the labels of Pilot Reports(PIREP)annotated data,where each sample contributes to the prediction result.By measuring the distance between the current CAT diagnostic variable and other variables,the model determines the climatically most similar neighbors and identifies the turbulence intensity category caused by the current variable.To evaluate the model’s performance in diagnosing high-altitude turbulence over Colorado,PIREP cases were randomly selected for analysis.The results show that the weighted KNN(W-KNN)model exhibits higher skill in turbulence prediction,and outperforms traditional prediction methods and other machine learning models(e.g.,Random Forest)in capturing moderate or greater(MOG)level turbulence.The performance of the model was confirmed by evaluating the receiver operating characteristic(ROC)curve,maximum True Skill Statistic(maxTSS=0.552),and reliability plot.A robust score(area under the curve:AUC=0.86)was obtained,and the model demonstrated sensitivity to seasonal and annual climate fluctuations.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.2017YFC0403605 and No.11601419).
文摘Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.
基金supported by the Innovative Research Groups of National Natural Science Foundation of China(No. 51621092)National Basic Research Program of China ("973" Program, No. 2013CB035904)National Natural Science Foundation of China (No. 51439005)
文摘During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.
基金This research was supported in part by the National Key Research and Development Program of China under Grant 2018YFC2001300in part by the National Natural Science Foundation of China under Grant 91948302,Grant 91848204,and Grant 52021003the Project of Scientific and Technological Development Plan of Jilin Province under Grant 20220508130RC.
文摘In this article,a new optimization system that uses few features to recognize locomotion with high classification accuracy is proposed.The optimization system consists of three parts.First,the features of the mixed mechanical signal data are extracted from each analysis window of 200 ms after each foot contact event.Then,the Binary version of the hybrid Gray Wolf Optimization and Particle Swarm Optimization(BGWOPSO)algorithm is used to select features.And,the selected features are optimized and assigned different weights by the Biogeography-Based Optimization(BBO)algorithm.Finally,an improved K-Nearest Neighbor(KNN)classifier is employed for intention recognition.This classifier has the advantages of high accuracy,few parameters as well as low memory burden.Based on data from eight patients with transfemoral amputations,the optimization system is evaluated.The numerical results indicate that the proposed model can recognize nine daily locomotion modes(i.e.,low-,mid-,and fast-speed level-ground walking,ramp ascent/decent,stair ascent/descent,and sit/stand)by only seven features,with an accuracy of 96.66%±0.68%.As for real-time prediction on a powered knee prosthesis,the shortest prediction time is only 9.8 ms.These promising results reveal the potential of intention recognition based on the proposed system for high-level control of the prosthetic knee.
基金the National Natural Science Foundation of China under projects 61772150 and 61862012the Guangxi Key R&D Program under project AB17195025+5 种基金the Guangxi Natural Science Foundation under grants 2018GXNSFDA281054 and 2018GXNSFAA281232the National Cryptography Development Fund of China under project MMJJ20170217the Guangxi Science and Technology Base and Special Talents Program AD18281044the Innovation Project of GUET Graduate Education under project 2017YJCX46the Guangxi Young Teachers’ Basic Ability Improvement Program under Grant 2018KY0194the open program of Guangxi Key Laboratory of Cryptography and Information Security under projects GCIS201621 and GCIS201702.
文摘Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy.
基金supported in part by the Basic Research Program of the Educational Department of Liaoning Province(Grant No.JYTMS20230802)the Natural Science Foundation of Liaoning Province(Grant No.2023-MS-317)the China Postdoctoral Science Foundation(Grant No.2021M701537).
文摘Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining.Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimizing the FS problem.The White Shark Optimizer(WSO),as a metaheuristic algorithm,primarily simulates the behavior of great white sharks’sense of hearing and smelling during swimming and hunting.However,it fails to consider their other randomly occurring behaviors,for example,Tail Slapping and Clustered Together behaviors.The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm.The Clustered Together behavior includes access to food and mating,which can change the direction of local search and enhance local utilization.It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer(IWSO).The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions,and the test results of IWSO are compared with other metaheuristic algorithms,which proves that IWSO combining the two behaviors has a stronger search capability.Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model,which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor(KNN)on 16 benchmark datasets and the results are compared with 7 metaheuristics.Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.
基金supported by the National Science Fund for Distinguished Young Scholars of China(61525304)the National Natural Science Foundation of China(61873328)
文摘In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.
文摘The EM algorithm is a very popular maximum likelihood estimation method, the iterative algorithm for solving the maximum likelihood estimator when the observation data is the incomplete data, but also is very effective algorithm to estimate the finite mixture model parameters. However, EM algorithm can not guarantee to find the global optimal solution, and often easy to fall into local optimal solution, so it is sensitive to the determination of initial value to iteration. Traditional EM algorithm select the initial value at random, we propose an improved method of selection of initial value. First, we use the k-nearest-neighbor method to delete outliers. Second, use the k-means to initialize the EM algorithm. Compare this method with the original random initial value method, numerical experiments show that the parameter estimation effect of the initialization of the EM algorithm is significantly better than the effect of the original EM algorithm.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
文摘The Bugaji area,situated within the Malumfashi Schist Belt of northwestern Nigeria,primarily consists of metasediments that include quartzo-feldspathic and pelitic schists,and gneiss.However,this area poses a challenge in mineral exploration due to limited outcrop exposures and complex subsurface structures.Hence,there is the need for exhaustive geophysical studies and supplementary approaches to accurately delineate lithologies and structures.Therefore,this study combines field mapping and geophysical techniques with artificial intelligence(AI)modeling,comprising supervised learning algorithms,to overcome this exploration problem.Utilizing sophisticated AI techniques,specifically the Random Forest Classifier and K-Nearest Neighbor algorithms,geophysical data(gravity,magnetic,and radiometric measurements)were processed and analyzed.The AI model effectively filled data gaps,and identified potential lithological variations and prospective mineralization zones based on geophysical signatures derived from the integrated dataset.The AI modeling's commendable average accuracy of 85%in predicting values underscores its efficacy in interpreting geophysical data.The success of random forest in the geological mapping process can be attributed to its ability to handle high-dimensional data,capture non-linear relationships between input variables,and mitigate overfitting.The integrated approach enhanced our understanding of subsurface geology in the Bugaji area.
基金Supported by the Nanjing University of Aeronautics and Astronautics(KFB2305601).
文摘The complexity and unpredictability of clear air turbulence(CAT)pose significant challenges to aviation safety.Accurate prediction of turbulence events is crucial for reducing flight accidents and economic losses.However,traditional turbulence prediction methods,such as ensemble forecasting techniques,have certain limitations:they only consider turbulence data from the most recent period,making it difficult to capture the nonlinear relationships present in turbulence.This study proposes a turbulence forecasting model based on the K-nearest neighbor(KNN)algorithm,which uses a combination of eight CAT diagnostic features as the feature vector and introduces CAT diagnostic feature weights to improve prediction accuracy.The model calculates the results of seven years of CAT diagnostics from 125 to 500 hPa obtained from the ECMWF fifth-generation reanalysis dataset(ERA5)as feature vector inputs and combines them with the labels of Pilot Reports(PIREP)annotated data,where each sample contributes to the prediction result.By measuring the distance between the current CAT diagnostic variable and other variables,the model determines the climatically most similar neighbors and identifies the turbulence intensity category caused by the current variable.To evaluate the model’s performance in diagnosing high-altitude turbulence over Colorado,PIREP cases were randomly selected for analysis.The results show that the weighted KNN(W-KNN)model exhibits higher skill in turbulence prediction,and outperforms traditional prediction methods and other machine learning models(e.g.,Random Forest)in capturing moderate or greater(MOG)level turbulence.The performance of the model was confirmed by evaluating the receiver operating characteristic(ROC)curve,maximum True Skill Statistic(maxTSS=0.552),and reliability plot.A robust score(area under the curve:AUC=0.86)was obtained,and the model demonstrated sensitivity to seasonal and annual climate fluctuations.
